Author: "Distribution, Recherche d'Information et Mobilité (DRIM)" / Topic: 02 engineering and technology - Searchworks@Jio Institute Digital Library Search Results

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209 results on '"Distribution, Recherche d'Information et Mobilité (DRIM)"'

1. Design Choices for X-vector Based Speaker Anonymization

Author: Xin Wang, Junichi Yamagishi, Brij Mohan Lal Srivastava, Marc Tommasi, Emmanuel Vincent, Aurélien Bellet, Natalia A. Tomashenko, Mohamed Maouche, Machine Learning in Information Networks (MAGNET), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, National Institute of Informatics (NII), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université de Lille, International Speech Communication Association (ISCA), Grid'5000, ANR-18-CE23-0018,DEEP-PRIVACY,Apprentissage distribué, personnalisé, préservant la privacité pour le traitement de la parole(2018), European Project: 825081,H2020,COMPRISE(2018), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique [Inria], Laboratórios de PesquIsa em ComputAção [LIA], National Institute of Informatics [NII], Speech Modeling for Facilitating Oral-Based Communication [MULTISPEECH], Distribution, Recherche d'Information et Mobilité [DRIM], Machine Learning in Information Networks [MAGNET], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille, Laboratórios de PesquIsa em ComputAção (LIA), Universidade Federal do Ceará = Federal University of Ceará (UFC), GRID5000, and ANR-18-CE23-0018,DEEP-PRIVACY,DISTRIBUTED, PERSONALIZED, PRIVACY-PRESERVING LEARNING FOR SPEECH PROCESSING(2018)
Subjects: FOS: Computer and information sciences, Scheme (programming language), speaker anonymization, voice conversion, Computer science, VoicePrivacy challenge, Word error rate, 02 engineering and technology, Space (commercial competition), computer.software_genre, 01 natural sciences, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Baseline (configuration management), 010301 acoustics, Selection (genetic algorithm), computer.programming_language, Computer Science - Computation and Language, 020206 networking & telecommunications, PLDA, x-vectors, Data mining, Computation and Language (cs.CL), computer, Decoding methods, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker. In this paper, we present a flexible pseudo-speaker selection technique as a baseline for the first VoicePrivacy Challenge. We explore several design choices for the distance metric between speakers, the region of x-vector space where the pseudo-speaker is picked, and gender selection. To assess the strength of anonymization achieved, we consider attackers using an x-vector based speaker verification system who may use original or anonymized speech for enrollment, depending on their knowledge of the anonymization scheme. The Equal Error Rate (EER) achieved by the attackers and the decoding Word Error Rate (WER) over anonymized data are reported as the measures of privacy and utility. Experiments are performed using datasets derived from LibriSpeech to find the optimal combination of design choices in terms of privacy and utility.
Published: 2020

2. Privacy in Big Data

Author: Nadia Bennani, Harald Kosch, Ernesto Damiani, Omar Hasan, Benjamin Habegger, Lionel Brunie, Thomas Cerqueus, Hasan, Omar, Distribution, Recherche d'Information et Mobilité ( DRIM ), Laboratoire d'InfoRmatique en Image et Systèmes d'information ( LIRIS ), Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Lipides - Nutrition - Cancer (U866) ( LNC ), Université de Bourgogne ( UB ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon ( ENSBANA ), Faculty of Informatics and Mathematics ( FMI ), Fakultät für Informatik und Mathematik, Università degli Studi di Milano-Bicocca [Milano], Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Lipides - Nutrition - Cancer (U866) (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Ecole Nationale Supérieure de Biologie Appliquée à la Nutrition et à l'Alimentation de Dijon (ENSBANA), Faculty of Informatics and Mathematics (FMI), and Università degli Studi di Milano-Bicocca [Milano] (UNIMIB)
Subjects: [ INFO ] Computer Science [cs], Computer science, business.industry, Internet privacy, Big data, 02 engineering and technology, [INFO] Computer Science [cs], World Wide Web, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, business, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2016

3. Toward Architectural and Protocol-Level Foundation for End-to-End Trustworthiness in Cloud/Fog Computing

Author: Houbing Song, Ziyi Su, Yong Peng, Zhihan Lv, Jingwei Miao, Frédérique Biennier, Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), China Information Technology Security Evaluation Center, Security and Optimization for Networked Globe Laboratory (West Virginia University Institute of Technology) (SONG Lab), Distribution, Recherche d'Information et Mobilité (DRIM), and Jilin Science Technology Devel-opment Project, 20140520074JH
Subjects: Information Systems and Management, Dataflow, Business process, Computer science, Distributed computing, Cloud/Fog Computing, Access control, Cloud computing, Context (language use), 02 engineering and technology, Computer security, computer.software_genre, Attribute-based access control, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], 0202 electrical engineering, electronic engineering, information engineering, Edge computing, Cloud computing security, end-to-end trustworthiness, business.industry, [INFO.INFO-WB]Computer Science [cs]/Web, Context, Computational modeling, 020206 networking & telecommunications, Collaboration, Data derivation, Data aggregator, Security Service Level Agreement, 020201 artificial intelligence & image processing, business, computer, Information Systems
Abstract: International audience; With Cloud/Fog Computing being a paradigm combination of IoT context and Edge Computing extended with Cloud/Fog, business process in it involves dataflows among multilayers and multi-nodes, possibly provided by multi-organizations. Achieving end-to-end trustworthiness over the whole dataflow in such a Cloud/Fog Computing context is a challenging issue, nonetheless a necessary pre-condition for a successful business process on intra-/inter- organizational level. This paper investigates technical conundrums related to this target and proposes a policy-based approach for trustworthiness governance. An architectural layout is proposed with according modules, by carrying out two methodologies. One resides in tracing data derivation and maintaining security-level over the whole dataflow, handling data aggregation with several protocols. The other is to express data owner trustworthiness requirements with an enhanced attribute-based access control policy model and to evaluate data accessing nodes’ trustworthiness-related properties. Experiments show that processing time per attribute pair drops as the scales of policies increase, suggesting good scaling property of the system.
Published: 2022

4. DataConf: A full client-side Web mashup for scientific conferences

Author: Florian Bacle, Nicolas Armando, Benoît Durant de la Pastellière, Fiona Le Peutrec, Lionel Médini, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Distribution, Recherche d'Information et Mobilité ( DRIM ), Laboratoire d'InfoRmatique en Image et Systèmes d'information ( LIRIS ), Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), and Université Claude Bernard Lyon 1 ( UCBL )
Subjects: medicine.medical_specialty, [ INFO ] Computer Science [cs], Computer science, Mobile Web, 02 engineering and technology, Linked data, Client-side, JavaScript, computer.software_genre, Web API, World Wide Web, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Mashup, [INFO]Computer Science [cs], Web service, computer, Web modeling, computer.programming_language
Abstract: International audience; This paper describes DataConf, a mobile Web mashup application that mixes Linked Data and Web APIs to provide access to different kinds of data. It relies on a widely used JavaScript framework and on a component-based approach to manage different datasources. It only requires static server-side contents and performs all processing on the client side.DataConf aggregates conference metadata. It allows browsing conference publications, publication authors, authors’ organizations, but also authors’ other publications, publications related to the same keywords, conference schedule or resources related to the conference publications. For this, it queries the SPARQL endpoint that serves the conference dataset, as well as other open or custom endpoints and Web APIs that enrich these data.DataConf is deployable for any conference with available metadata on the Web using a configuration file. Other data sources can be used by developing and plugging new components in the DataConf architecture.
Published: 2013

5. Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based Approach

Author: Nicolas Marchand, Antoine Boutet, Sophie Cerf, Sonia Ben Mokhtar, Vincent Primault, Lydia Y. Chen, Sara Bouchenak, Bogdan Robu, GIPSA - Systèmes non linéaires et complexité (GIPSA-SYSCO), Département Automatique (GIPSA-DA), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), IBM Research [Zurich], Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Information privacy, D48b Modeling and prediction, Computer science, Distributed computing, media_common.quotation_subject, Usability, 0211 other engineering and technologies, Index Terms-D46 Security and Privacy Protection, 02 engineering and technology, Configuration control, J9a Location-dependent and sensitive, D216b Configu- ration control, Adaptability, Data modeling, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], Robustness (computer science), [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, Privacy protection, Electrical and Electronic Engineering, media_common, Measurement, 021110 strategic, defence & security studies, business.industry, H20a Security, Adaptation models, Computational modeling, and protec- tion, Security, integrity, business, Data privacy, Personally identifiable information, Mobile device, Protection mechanism
Abstract: International audience; The widespread use of mobile devices and location-based services has generated a large number of mobility databases. While processing these data is highly valuable, privacy issues can occur if personal information is revealed. The prior art has investigated ways to protect mobility data by providing a wide range of Location Privacy Protection Mechanisms (LPPMs). However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful processing. To tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and automatically configures it for each user in order to achieve user-defined objectives in terms of both privacy and utility. PULP uses nonlinear models to capture the impact of each LPPM on data privacy and utility levels. Evaluation of our framework is carried out with two protectionmechanisms from the literature and four real-world mobility datasets. Results show the efficiency of PULP, its robustness and adaptability. Comparisons between LPPMs’ configurators and the state of the art further illustrate that PULP better realizes users’ objectives, and its computation time is in orders of magnitude faster.
Published: 2021

6. Anytime mining of sequential discriminative patterns in labeled sequences

Author: Jean-François Boulicaut, Mehdi Kaytoue, Diana Nurbakova, Romain Mathonat, Data Mining and Machine Learning (DM2L), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Distribution, Recherche d'Information et Mobilité (DRIM)
Subjects: Monte Carlo Tree Search, Computer science, Monte Carlo tree search, 02 engineering and technology, Machine learning, computer.software_genre, Multi-armed bandit, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Local optimum, Discriminative model, Artificial Intelligence, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Subgroup Discovery, Class (computer programming), business.industry, Sampling (statistics), Pattern Mining, Predictive analytics, Upper Confidence Bound, Multi-Armed Bandit, Human-Computer Interaction, Hardware and Architecture, Anytime algorithm, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Software, Information Systems
Abstract: It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of patterns covering sets of objects having interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for both transactional and numerical data, discovering subgroups within labeled sequential data has been much less studied. First, we propose an anytime algorithm SeqScout that discovers interesting subgroups w.r.t. a chosen quality measure. This is a sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. For a given budget, it finds a collection of local optima in the search space of descriptions and thus, subgroups. It requires a light configuration and is independent from the quality measure used for pattern scoring. We also introduce a second anytime algorithm MCTSExtent that pushes further the idea of a better trade-off between exploration and exploitation of a sampling strategy over the search space. To the best of our knowledge, this is the first time that the Monte Carlo Tree Search framework is exploited in a sequential data mining setting. We have conducted a thorough and comprehensive evaluation of our algorithms on several datasets to illustrate their added value, and we discuss their qualitative and quantitative results.
Published: 2020

7. EDEN: Enforcing Location Privacy through Re-identification Risk Assessment: A Federated Learning Approach

Author: Sonia Ben Mokhtar, Sara Bouchenak, Besma Khalfoun, Vlad Nitu, Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Distribution, Recherche d'Information et Mobilité (DRIM), and Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Subjects: Computer Networks and Communications, Computer science, Protection Mechanism, Context (language use), 02 engineering and technology, Computer security, computer.software_genre, Set (abstract data type), [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Mobility Data, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Selection (linguistics), TRACE (psycholinguistics), 020206 networking & telecommunications, Proxy server, Variety (cybernetics), Human-Computer Interaction, Information sensitivity, Re-identification Attack, Hardware and Architecture, Data utility, Crowd Sensing Applications, Location Privacy, computer, Federated Learning, Protection mechanism
Abstract: International audience; Crowd sensing applications have demonstrated their usefulness in many real-life scenarios (e.g., air quality monitoring, traffic and noise monitoring). Preserving the privacy of crowd sensing app users is becoming increasingly important as the collected geo-located data may reveal sensitive information about these users (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. However, each LPPM comes with a given set of configuration parameters. The value of these parameters impacts not only the privacy level but also the utility of the resulting data. Choosing the right LPPM and the right configuration for reaching a satisfactory privacy vs. utility tradeoff is generally a difficult problem mobile app developers have to face. Solving this problem is commonly done by relying on a trusted proxy server to which raw geo-located traces are sent and privacy vs. utility assessment is performed enabling the selection of the best LPPM for each trace. In this paper we present EDEN, the first solution that selects automatically the best LPPM and its corresponding configuration without sending raw geo-located traces outside the user's device. We reach this objective by relying on a federated learning approach. The evaluation of EDEN on five real-world mobility datasets shows that EDEN outperforms state-of-the-art LPPMs reaching a better privacy vs. utility tradeoff.
Published: 2021

8. The Long Road to Computational Location Privacy: A Survey

Author: Primault Vincent, Ben Mokhtar Sonia, Boutet Antoine, Brunie Lionel, Department of Computer science [University College of London] (UCL-CS), University College of London [London] (UCL), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Computer science department [University College London] (UCL-CS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: FOS: Computer and information sciences, Online and offline, Measurement, Computer Science - Cryptography and Security, Computer science, business.industry, Internet privacy, Tutorials, 02 engineering and technology, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Information sensitivity, Meteorology, Work (electrical), Privacy, 020204 information systems, Assisted GPS, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Electrical and Electronic Engineering, Games, business, Cryptography and Security (cs.CR), Real-time systems, Data privacy, Mobile device
Abstract: The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed., Comment: IEEE Communications Surveys & Tutorials
Published: 2019

9. Computer Aided Formal Design of Swarm Robotics Algorithms

Author: Xavier Urbain, Pierre Courtieu, Thibaut Balabonski, Lionel Rieg, Robin Pelle, Sébastien Tixeuil, Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CEDRIC. Systèmes sûrs (CEDRIC - SYS), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), VERIMAG (VERIMAG - IMAG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Networks and Performance Analysis (NPA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratory of Information, Network and Communication Sciences (LINCS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT)-Sorbonne Université (SU), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Laboratoire Méthodes Formelles (LMF), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), ANR-19-CE25-0005,SAPPORO,Sûreté et preuve de protocoles adaptatifs pour robots oublieux(2019), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computational Geometry (cs.CG), FOS: Computer and information sciences, Theoretical computer science, Correctness, Discrete Mathematics (cs.DM), Computer science, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Swarm robotics, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], 01 natural sciences, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, Development (topology), [INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL], 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Use case, ComputingMilieux_MISCELLANEOUS, Proof assistant, 020207 software engineering, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, Formal methods, Computer Science - Distributed, Parallel, and Cluster Computing, 010201 computation theory & mathematics, Benchmark (computing), Computer-aided, Computer Science - Computational Geometry, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Computer Science - Discrete Mathematics
Abstract: Previous works on formally studying mobile robotic swarms consider necessary and sufficient system hypotheses enabling to solve theoretical benchmark problems (geometric pattern formation, gathering, scattering, etc.). We argue that formal methods can also help in the early stage of mobile robotic swarms protocol design, to obtain protocols that are correct-by-design, even for problems arising from real-world use cases, not previously studied theoretically. Our position is supported by a concrete case study. Starting from a real-world case scenario, we jointly design the formal problem specification, a family of protocols that are able to solve the problem, and their corresponding proof of correctness, all expressed with the same formal framework. The concrete framework we use for our development is the PACTOLE library based on the COQ proof assistant.
Published: 2021

10. GraphSIF: analyzing flow of payments in a Business-to-Business network to detect supplier impersonation

Author: Omar Hasan, Rémi Canillas, Lionel Brunie, Laurent Sarrat, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Distribution, Recherche d'Information et Mobilité (DRIM), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)
Subjects: Computer Networks and Communications, Computer science, Accurate estimation, media_common.quotation_subject, B2B network, 02 engineering and technology, Computer security, computer.software_genre, Financial networks, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS, Graph-based feature engineering, media_common, Multidisciplinary, business.industry, lcsh:T57-57.97, Business-to-business, Payment, Expert system, Computational Mathematics, Fraud detection, Financial transaction, lcsh:Applied mathematics. Quantitative methods, Graph (abstract data type), 020201 artificial intelligence & image processing, Anomaly detection, business, computer, Database transaction
Abstract: Supplier Impersonation Fraud (SIF) is a rising issue for Business-to-Business companies. The use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose a data-driven fraud detection system whose goal is to provide an accurate estimation of financial transaction legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its suppliers. We consider the real dataset collected by SIS-ID for this work.We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent based on its similarity with frequently occurring transactions for a given company. Experiments demonstrate that our approach shows high consistency with expert knowledge on a real-life dataset, while performing faster than the expert system.
Published: 2020

11. Recommandation diversifiée via des processus ponctuels déterminantaux sur des graphes de connaissances

Author: Diana Nurbakova, Léa Laporte, Lu Gan, Sylvie Calabretto, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: Structure (mathematical logic), Diversity, Information retrieval, Knowledge Graph, Computer science, Recommender Systems, 02 engineering and technology, Recommender system, MovieLens, Determinantal Point Processes, Kernel (image processing), Margin (machine learning), 020204 information systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Embedding, 020201 artificial intelligence & image processing, Construct (philosophy), Diversity (business)
Abstract: International audience; Top-N recommendations are widely applied in various real life domains and keep attracting intense attention from researchers and industry due to available multi-type information, new advances in AI models and deeper understanding of user satisfaction.While accuracy has been the prevailing issue of the recommendation problem for the last decades, other facets of the problem, namely diversity and explainability, have received much less attention. In this paper, we focus on enhancing diversity of top-N recommendation, while ensuring the trade-off between accuracy and diversity. Thus, we propose an effective framework DivKG leveraging knowledge graph embedding and determinantal point processes (DPP). First, we capture different kinds of relations among users, items and additional entities through a knowledge graph structure. Then, we represent both entities and relations as k-dimensional vectors by optimizing a margin-based loss with all kinds of historical interactions. We use these representations to construct kernel matrices of DPP in order to make top-N diversified predictions. We evaluate our framework on MovieLens datasets coupled with IMDb dataset. Our empirical results show substantial improvement over the state-of-the-art regarding both accuracy and diversity metrics.
Published: 2020

12. Towards Practical Privacy-Preserving Collaborative Machine Learning at a Scale

Author: Rania Talbi, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: 021110 strategic, defence & security studies, 0303 health sciences, Computer science, business.industry, Data classification, 0211 other engineering and technologies, Homomorphic encryption, Inference, Collaborative learning, 02 engineering and technology, Encryption, Machine learning, computer.software_genre, 03 medical and health sciences, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Information leakage, Confidentiality, [INFO]Computer Science [cs], Artificial intelligence, State (computer science), business, computer, 030304 developmental biology
Abstract: International audience; Collaborative machine learning allows multiple participants to get a global and valuable insight over their joint data. Nonetheless, in data-sensitive applications, it is crucial to maintain confidentiality across the end-to-end path the data follows from model training phase to the inference phase, preventing any form of information leakage about training data, the learned model, or the inference queries. In this paper, we present our approach to address this problem through PrivML, a framework for end-to-end outsourced privacy-preserving data classification over encrypted data. We provide some preliminary results comparing our proposal with state of the art solutions as well as some insight on our prospective research plan.
Published: 2020

13. TailX: Scheduling Heterogeneous Multiget Queries to Improve Tail Latencies in Key-Value Stores

Author: Jaiman, Vikas, Mokhtar, Sonia Ben, Rivière, Etienne, Remke, A., Schiavoni, V., Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université Catholique de Louvain = Catholic University of Louvain (UCL), Institute of Data Science, RS: FSE DACS IDS, and UCL - SST/ICTM/INGI - Pôle en ingénierie informatique
Subjects: 050101 languages & linguistics, Schedule, Computer science, business.industry, Scheduling, Distributed storage, Performance, 05 social sciences, Cloud computing, 02 engineering and technology, Bottleneck, Article, Scheduling (computing), [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Cloud data, Server, Distributed data store, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, [INFO]Computer Science [cs], Latency (engineering), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Computer network
Abstract: International audience; Users of interactive services such as e-commerce platforms have high expectations for the performance and responsiveness of these services. Tail latency, denoting the worst service times, contributes greatly to user dissatisfaction and should be minimized. Maintaining low tail latency for interactive services is challenging because a request is not complete until all its operations are completed. The challenge is to identify bottleneck operations and schedule them on uncoordinated backend servers with minimal overhead, when the duration of these operations are heterogeneous and unpredictable. In this paper, we focus on improving the latency of multiget operations in cloud data stores. We present TailX, a task-aware multiget scheduling algorithm that improves tail latencies under heterogeneous workloads. TailX schedules operations according to an estimation of the size of the corresponding data, and allows itself to procrastinate some operations to give way to higher priority ones. We implement TailX in Cassandra, a widely used key-value store. The result is an improved overall performance of the cloud data stores for a wide variety of heterogeneous workloads. Specifically, our experiments under heterogeneous YCSB workloads show that TailX outperforms state-of-the-art solutions and reduces tail latencies by up to 70% and median latencies by up to 75%.
Published: 2020

14. Supplier Impersonation Fraud Detection using Bayesian Inference

Author: Lionel Brunie, Rémi Canillas, Omar Hasan, Laurent Sarrat, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Université de Lyon-Institut National des Sciences Appliquées (INSA)
Subjects: Computer science, media_common.quotation_subject, 05 social sciences, 050401 social sciences methods, 02 engineering and technology, computer.software_genre, Payment, Bayesian inference, Expert system, Set (abstract data type), 0504 sociology, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, Statistical analysis, Data mining, computer, Database transaction, ComputingMilieux_MISCELLANEOUS, media_common
Abstract: In this paper, we introduce ProbaSIF, a supplier impersonation fraud detection system that relies on a Bayesian model to perform the classification of a new transaction as legitimate or fraudulent. ProbaSIF is divided in two parts: an intra-company analysis that aims to recreate the vision of a specific client about the legitimacy of the account used in a transaction with one of its supplier, and an inter-company analysis that uses all the accounts used to pay a supplier to model the supplier's payment behavior and take into account transactions issued by other clients. We use a dataset composed of more than 2 million transactions issued by real companies, provided by the SiS-id platform, to fit our Bayesian model, and evaluate the classification results of ProbaSIF using an other set of 108,000 transactions labeled by SiS-id expert system. Our study of a representative client shows that both of the approaches described in ProbaSIF show good precision (0.927 and 0.836) for the 255 transactions tested. Results also shows that ProbaSIF gives results consistent with the expert system provided by SiS-id. Finally, after evaluating ProbaSIF approaches on all the clients available in our dataset, we demonstrated that our classification system was accurate for a wide set of different clients.
Published: 2020

15. Towards an Inference Detection System Against Multi-database Attacks

Author: Nadia Bennani, Veronika Rehn-Sonigo, Paul Lachat, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), University of Passau, Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)
Subjects: data privacy, 050101 languages & linguistics, Information privacy, Database, Computer science, 05 social sciences, Probabilistic logic, Inference, 02 engineering and technology, inference detection system, Inference attack, computer.software_genre, Global model, Proof of concept, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, computer, database, Record linkage
Abstract: International audience; Nowadays, users are permanently prompted to create web accounts when they buy online goods. This collected data gives an insight on the user, sometimes beyond the application scope. Inference attacks on databases represent an issue for data controllers when malicious processors attempt to guess sensitive data - to which they haven’t access - by inferring them using legally accessed data. Several inference attack detection systems address this problem in case of a single targeted database. But the issue remains unsolved in case of several databases to which the same users might have submitted their data. In this paper, we propose a global model and its associated graph representation named Global Instance Graph (GIG) representing the probabilistic and semantic dependencies inside each database, enriched by the dependencies between the different databases. The graph is obtained using privacy-preserving record linkage techniques and serves as a knowledge input to the inference attack detection system. We validate the GIG creation feasibility thanks to a proof of concept. Despite the quadratic creation time, the performances when data is queried from the databases are not affected since the GIG creation is performed offline.
Published: 2020

16. An Exploratory Analysis on Users' Contributions in Federated Learning

Author: Jiyue Huang, Rania Talbi, Sara Boucchenak, Zilong Zhao, Lydia Y. Chen, Stefanie Roos, Delft University of Technology (TU Delft), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Distribution, Recherche d'Information et Mobilité (DRIM), and Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Subjects: FOS: Computer and information sciences, 020203 distributed computing, Measure (data warehouse), Information privacy, Computer science, Existential quantification, Cognitive neuroscience of visual object recognition, Adversarial Behavior, 020206 networking & telecommunications, Collaborative learning, 02 engineering and technology, Data science, Data modeling, Core (game theory), [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Incentive, Computer Science - Distributed, Parallel, and Cluster Computing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Contribution Measurement, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Distributed, Parallel, and Cluster Computing (cs.DC), Incentive Mechanisms, Federated Learning, ComputingMilieux_MISCELLANEOUS
Abstract: Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose low-quality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives.
Published: 2020

17. Feedback Autonomic Provisioning for Guaranteeing Performance in MapReduce Systems

Author: Bogdan Robu, Damián Serrano, Sara Bouchenak, Nicolas Marchand, Mihaly Berekmeri, GIPSA - Systèmes non linéaires et complexité (GIPSA-SYSCO), Département Automatique (GIPSA-DA), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Grid'5000, ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), European Project: 610535,EC:FP7:ICT,FP7-ICT-2013-10,AMADEOS(2013), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Computer Networks and Communications, business.industry, Computer science, Distributed computing, Node (networking), Big data, Feed forward, Workload, Provisioning, Cloud computing, 02 engineering and technology, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Computer Science Applications, Data modeling, 020901 industrial engineering & automation, Hardware and Architecture, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, business, Software, Information Systems
Abstract: International audience; Companies have a fast growing amounts of data to process and store, a data explosion is happening next to us. Currentlyone of the most common approaches to treat these vast data quantities are based on the MapReduce parallel programming paradigm.While its use is widespread in the industry, ensuring performance constraints, while at the same time minimizing costs, still providesconsiderable challenges. We propose a coarse grained control theoretical approach, based on techniques that have already provedtheir usefulness in the control community. We introduce the first algorithm to create dynamic models for Big Data MapReduce systems,running a concurrent workload. Furthermore we identify two important control use cases: relaxed performance - minimal resourceand strict performance. For the first case we develop two feedback control mechanism. A classical feedback controller and an evenbasedfeedback, that minimises the number of cluster reconfigurations as well. Moreover, to address strict performance requirements afeedforward predictive controller that efficiently suppresses the effects of large workload size variations is developed. All the controllersare validated online in a benchmark running in a real 60 node MapReduce cluster, using a data intensive Business Intelligenceworkload. Our experiments demonstrate the success of the control strategies employed in assuring service time constraints.
Published: 2018

18. MooD: MObility Data Privacy as Orphan Disease -Experimentation and Deployment Paper

Author: Sara Bouchenak, Sonia Ben Mokhtar, Mohamed Maouche, Besma Khalfoun, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: Information privacy, Computer science, business.industry, Data Privacy, Internet privacy, User-Centric Protection, 020206 networking & telecommunications, 02 engineering and technology, Competitor analysis, Location Privacy Protection Mechanism, Medical research, Information sensitivity, User Re-identification, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Mood, Mobility Data, Software deployment, 020204 information systems, Location-based service, 0202 electrical engineering, electronic engineering, information engineering, business, Mobile device
Abstract: International audience; With the increasing development of handheld devices, Location Based Services (LBSs) became very popular in facilitating users' daily life with a broad range of applications (e.g. traffic monitoring, geo-located search, geo-gaming). However , several studies have shown that the collected mobility data may reveal sensitive information about end-users such as their home and workplaces, their gender, political, religious or sexual preferences. To overcome these threats, many Location Privacy Protection Mechanisms (LPPMs) were proposed in the literature. While the existing LPPMs try to protect most of the users in mobility datasets, there is usually a subset of users who are not protected by any of the existing LPPMs. By analogy to medical research, there are orphan diseases, for which the medical community is still looking for a remedy. In this paper, we present MooD, a fine-grained multi-LPPM user-centric solution whose main objective is to find a treatment to mobile users' orphan disease by protecting them from re-identification attacks. Our experiments are conducted on four real world datasets. The results show that MooD outperforms its competitors, and the amount of user mobility data it is able to protect is in the range between 97.5% to 100% on the various datasets. CCS Concepts • Security and privacy → Pseudonymity, anonymity and untraceability.
Published: 2019

19. PrivaTube

Author: Da Silva, Simon, Ben Mokhtar, Sonia, Contiu, Stefan, Négru, Daniel, Réveillère, Laurent, Riviere, Etienne, Proceedings of the 20th International Middleware Conference, UCL - SST/ICTM/INGI - Pôle en ingénierie informatique, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université de Bordeaux (UB), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université Bordeaux Segalen - Bordeaux 2
Subjects: Computer science, security, 02 engineering and technology, privacy, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Server, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], streaming, Quality of experience, Keywords multimedia, business.industry, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, 020206 networking & telecommunications, Provisioning, CCS Concepts · Security and privacy, Internet traffic, TEE, Scalability, 020201 artificial intelligence & image processing, Enhanced Data Rates for GSM Evolution, Cache, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Personally identifiable information, Computer network
Abstract: International audience; Video on Demand (VoD) streaming is the largest source of Inter-net traffic. Efficient and scalable VoD requires Content Delivery Networks (CDNs) whose cost are prohibitive for many providers. An alternative is to cache and serve video content using end-users devices. Direct connections between these devices complement the resources of core VoD servers with an edge-assisted collaborative CDN. VoD access histories can reveal critical personal information, and centralized VoD solutions are notorious for exploiting personal data. Hiding the interests of users from servers and edge-assisting devices is necessary for a new generation of privacy-preserving VoD services. We introduce PrivaTube, a scalable and cost-effective VoD solution. PrivaTube aggregates video content from multiple servers and edge peers to offer a high Quality of Experience (QoE) for its users. It enables privacy preservation at all levels of the content distribution process. It leverages Trusted Execution Environments (TEEs) at servers and clients, and obfuscates access patterns using fake requests that reduce the risk of personal information leaks. Fake requests are further leveraged to implement proactive provisioning and improve QoE. Our evaluation of a complete prototype shows that PrivaTube reduces the load on servers and increases QoE while providing strong privacy guarantees.
Published: 2019

20. A Transparent Referendum Protocol with Immutable Proceedings and Verifiable Outcome for Trustless Networks

Author: Harald Kosch, Lionel Brunie, Maximilian Schiedermeier, Tobias R. Mayer, Omar Hasan, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université de Lyon-Institut National des Sciences Appliquées (INSA), Faculty of Informatics and Mathematics (FMI), and Fakultät für Informatik und Mathematik
Subjects: FOS: Computer and information sciences, 021110 strategic, defence & security studies, 0303 health sciences, Immutability, Computer Science - Cryptography and Security, Electronic voting, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Computer security, computer.software_genre, Transparency (behavior), 03 medical and health sciences, Referendum, Confidentiality, Verifiable secret sharing, [INFO]Computer Science [cs], Polling, Cryptography and Security (cs.CR), computer, Protocol (object-oriented programming), ComputingMilieux_MISCELLANEOUS, 030304 developmental biology
Abstract: High voter turnout in elections and referendums is very desirable in order to ensure a robust democracy. Secure electronic voting is a vision for the future of elections and referendums. Such a system can counteract factors that hinder strong voter turnout such as the requirement of physical presence during limited hours at polling stations. However, this vision brings transparency and confidentiality requirements that render the design of such solutions challenging. Specifically, the counting must be implemented in a reproducible way and the ballots of individual voters must remain concealed. In this paper, we propose and evaluate a referendum protocol that ensures transparency, confidentiality, and integrity, in trustless networks. The protocol is built by combining Secure Multi-Party Computation (SMPC) and Distributed Ledger or Blockchain technology. The persistence and immutability of the protocol communication allows verifiability of the referendum outcome on the client side. Voters therefore do not need to trust in third parties. We provide a formal description and conduct a thorough security evaluation of our proposal., Comment: 14 pages, 3 figures
Published: 2019

21. Supplier Impersonation Fraud Detection in Business-To-Business Transaction Networks Using Self-Organizing Maps

Author: Lionel Brunie, Laurent Sarrat, Omar Hasan, Rémi Canillas, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Institut National des Sciences Appliquées (INSA), and Université de Lyon-Institut National des Sciences Appliquées (INSA)
Subjects: Self-organizing map, Knowledge management, Financial networks, business.industry, Computer science, Accurate estimation, 02 engineering and technology, Business-to-business, Task (project management), Work (electrical), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_COMPUTERSANDSOCIETY, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], business, Database transaction, Legitimacy, ComputingMilieux_MISCELLANEOUS
Abstract: Supplier Impersonation Fraud (SIF) is a rising issue for Business to Business companies, as the use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose data-driven fraud detection system whose goal is to provide an accurate estimation of transactions’ legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its supplier. We consider the real dataset collected by SIS-ID for this work.
Published: 2019

22. Synchronous Gathering without Multiplicity Detection: a Certified Algorithm

Author: Thibaut Balabonski, Amélie Delga, Lionel Rieg, Xavier Urbain, Sébastien Tixeuil, Université Paris-Saclay, Université Paris-Sud - Paris 11 (UP11), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Chaire Algorithmes, machines et langages, Collège de France (CdF (institution)), Networks and Performance Analysis (NPA), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Laboratory of Information, Network and Communication Sciences (LINCS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), Collège de France - Chaire Algorithmes, machines et langages, Vérification d'Algorithmes, Langages et Systèmes (LRI) (VALS - LRI), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science (YALE), Yale University [New Haven], VERIMAG (VERIMAG - IMAG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT)-Sorbonne Université (SU), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: ACM: F.: Theory of Computation/F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY/F.2.2: Nonnumerical Algorithms and Problems/F.2.2.0: Complexity of proof procedures, Correctness, Computer science, Distributed computing, 0102 computer and information sciences, 02 engineering and technology, Certification, 01 natural sciences, Theoretical Computer Science, Computer Science::Robotics, ACM: F.: Theory of Computation/F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY/F.2.2: Nonnumerical Algorithms and Problems/F.2.2.1: Computations on discrete structures, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Computer vision, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.4: Distributed Systems/C.2.4.1: Distributed applications, ACM: F.: Theory of Computation/F.3: LOGICS AND MEANINGS OF PROGRAMS/F.3.1: Specifying and Verifying and Reasoning about Programs/F.3.1.3: Mechanical verification, ACM: F.: Theory of Computation/F.3: LOGICS AND MEANINGS OF PROGRAMS/F.3.3: Studies of Program Constructs/F.3.3.4: Type structure, Mathematics, business.industry, Proof assistant, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Swarm behaviour, Mobile robot, Multiplicity (mathematics), ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.5: Reliability/D.4.5.2: Fault-tolerance, Computational Theory and Mathematics, 010201 computation theory & mathematics, Theory of computation, Robot, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Finite time, business, Algorithm
Abstract: International audience; In mobile robotic swarms, the gathering problem consists in coordinating all the robots so that in finite time they occupy the same location, not known beforehand. Multiplicity detection refers to the ability to detect that more than one robot can occupy a given position. When the robotic swarm operates synchronously, a well-known result by Cohen and Peleg permits to achieve gathering, provided robots are capable of multiplicity detection. We present a new algorithm for synchronous gathering, that does not assume that robots are capable of multiplicity detection, nor make any other extra assumption. Unlike previous approaches, the correctness of our proof is certified in the model where the protocol is defined, using the Coq proof assistant.
Published: 2018

23. 4PR: Privacy preserving routing in mobile delay tolerant networks

Author: Sonia Ben Mokhtar, Omar Hasan, Jingwei Miao, Lionel Brunie, Ammar Hasan, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: Routing protocol, Dynamic Source Routing, Mobility model, Computer Networks and Communications, Computer science, Equal-cost multi-path routing, Routing table, Enhanced Interior Gateway Routing Protocol, Mobile computing, Wireless Routing Protocol, Geographic routing, 02 engineering and technology, Routing Information Protocol, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Destination-Sequenced Distance Vector routing, ComputingMilieux_MISCELLANEOUS, Triangular routing, Zone Routing Protocol, Static routing, business.industry, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Policy-based routing, 020206 networking & telecommunications, Distance-vector routing protocol, Optimized Link State Routing Protocol, Routing domain, Link-state routing protocol, Interior gateway protocol, Multipath routing, 020201 artificial intelligence & image processing, business, Computer network
Abstract: Message routing is one of the major challenges in Mobile Delay Tolerant Networks (MDTNs) due to frequent and long-term network partitions. A number of routing protocols for MDTNs belong to the category of prediction-based routing protocols, which utilize the social encounter probability of nodes to guide message forwarding. However, these prediction-based routing protocols compromise the privacy of the nodes by revealing their mobility patterns. In this paper, we propose the Privacy Preserving Probabilistic Prediction-based Routing (4PR) protocol that forwards messages by comparing aggregated information about communities instead of individual nodes. Specifically, it compares the probability that at least one node in a community will encounter the destination node. We present theoretical security analyses as well as practical performance evaluations. Our simulations on a well established community-based mobility model demonstrate that our routing protocol has comparable performance to existing prediction-based protocols. Additionally, the community information is computed efficiently and independently of the routing protocol.
Published: 2016

24. Predicting Query Difficulty in IR: Impact of Difficulty Definition

Author: Adrian-Gabriel Chifu, Josiane Mothe, Léa Laporte, Systèmes d’Informations Généralisées (IRIT-SIG), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix-Marseille Université - AMU (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National des Sciences Appliquées de Lyon - INSA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université de Toulon - UTLN (FRANCE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: Query features, Information retrieval, Computer science, Gain measurement, Query difficulty prediction, Recherche d'information, 02 engineering and technology, System failure, Robustness (computer science), 020204 information systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing
Abstract: International audience; While it exists information on about any topic on the web, we know from information retrieval (IR) evaluation programs that search systems fail to answer to some queries in an effective manner. System failure is associated to query difficulty in the IR literature. However, there is no clear definition of query difficulty. This paper investigates several ways of defining query difficulty and analyses the impact of these definitions on query difficulty prediction results. Our experiments show that the most stable definition across collections is a threshold-based definition of query difficulty classes.
Published: 2019

25. SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences

Author: Mehdi Kaytoue, Jean-François Boulicaut, Romain Mathonat, Diana Nurbakova, Data Mining and Machine Learning (DM2L), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Distribution, Recherche d'Information et Mobilité (DRIM)
Subjects: business.industry, Heuristic, Computer science, Context (language use), 02 engineering and technology, Pattern Mining, Machine learning, computer.software_genre, Class (biology), Sequences, Upper Confidence Bound, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), Local optimum, 020204 information systems, Anytime algorithm, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Subgroup Discovery, Artificial intelligence, business, Transaction data, computer
Abstract: International audience; It is extremely useful to exploit labeled datasets not only to learn models but also to improve our understanding of a domain and its available targeted classes. The so-called subgroup discovery task has been considered for a long time. It concerns the discovery of patterns or descriptions, the set of supporting objects of which have interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for transactional data, discovering subgroups within labeled sequential data and thus searching for descriptions as sequential patterns has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. We propose the algorithm SeqScout to discover interesting subgroups (w.r.t. a chosen quality measure) from labeled sequences of itemsets. This is a new sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. It is an anytime algorithm that, for a given budget, finds a collection of local optima in the search space of descriptions and thus subgroups. It requires a light configuration and it is independent from the quality measure used for pattern scoring. Furthermore, it is fairly simple to implement. We provide qualitative and quantitative experiments on several datasets to illustrate its added-value.
Published: 2019

26. CrowdED and CREX: Towards Easy Crowdsourcing Quality Control Evaluation

Author: Lionel Brunie, Nadia Bennani, Veronika Rehn-Sonigo, Harald Kosch, Tarek Awwad, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), and University of Passau
Subjects: 050101 languages & linguistics, business.industry, Computer science, media_common.quotation_subject, 05 social sciences, Control (management), 02 engineering and technology, Crowdsourcing, Machine learning, computer.software_genre, Domain (software engineering), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Quality (business), [INFO]Computer Science [cs], Artificial intelligence, business, computer, Control methods, ComputingMilieux_MISCELLANEOUS, media_common
Abstract: Crowdsourcing is a time- and cost-efficient web-based technique for labeling large datasets like those used in Machine Learning. Controlling the output quality in crowdsourcing is an active research domain which has yielded a fair number of methods and approaches. Due to the quantitative and qualitative limitations of the existing evaluation datasets, comparing and evaluating these methods have been very limited. In this paper, we present CrowdED (Crowdsourcing Evaluation Dataset), a rich dataset for evaluating a wide range of quality control methods alongside with CREX (CReate Enrich eXtend), a framework that facilitates the creation of such datasets and guarantees their future-proofing and re-usability through customizable extension and enrichment.
Published: 2019

27. RACOON++: A Semi-Automatic Framework for the Selfishness-Aware Design of Cooperative Systems

Author: Gilles Muller, Lionel Brunie, Gabriele Gianini, Sonia Ben Mokhtar, Julia Lawall, Ernesto Damiani, Guido Lena Cota, Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Università degli Studi di Milano [Milano] (UNIMI), Well Honed Infrastructure Software for Programming Environments and Runtimes ( Whisper), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano = University of Milan (UNIMI), and Well Honed Infrastructure Software for Programming Environments and Runtimes (Whisper)
Subjects: 021110 strategic, defence & security studies, [INFO.INFO-GT]Computer Science [cs]/Computer Science and Game Theory [cs.GT], Computer science, media_common.quotation_subject, Distributed computing, 0211 other engineering and technologies, Evolutionary game theory, 02 engineering and technology, Load balancing (computing), Communications system, Declarative model, Live streaming, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Selfishness, Semi automatic, Electrical and Electronic Engineering, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Game theory, media_common
Abstract: International audience; A challenge in designing cooperative distributed systems is to develop feasible and cost-effective mechanisms to foster 7 cooperation among selfish nodes, i.e., nodes that strategically deviate from the intended specification to increase their individual utility. 8 Finding a satisfactory solution to this challenge may be complicated by the intrinsic characteristics of each system, as well as by the 9 particular objectives set by the system designer. Our previous work addressed this challenge by proposing RACOON, a general and 10 semi-automatic framework for designing selfishness-resilient cooperative systems. RACOON relies on classical game theory and a 11 custom built simulator to predict the impact of a fixed set of selfish behaviours on the designer's objectives. In this paper, we present 12 RACOON++, which extends the previous framework with a declarative model for defining the utility function and the static behaviour of 13 selfish nodes, along with a new model for reasoning on the dynamic interactions of nodes, based on evolutionary game theory. We 14 illustrate the benefits of using RACOON++ by designing three cooperative systems: a peer-to-peer live streaming system, a load 15 balancing protocol, and an anonymous communication system. Extensive experimental results using the state-of-the-art PeerSim 16 simulator verify that the systems designed using RACOON++ achieve both selfishness-resilience and high performance.
Published: 2019

28. Dataset shift quantification for credit card fraud detection

Author: Yvan Lucas, Liyun He-Guelton, Pierre-Edouard Portier, Frédéric Oblé, Léa Laporte, Sylvie Calabretto, Michael Granitzer, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Atos Worldline, Atos, and University of Passau
Subjects: FOS: Computer and information sciences, 0209 industrial biotechnology, Computer Science - Machine Learning, Concept drift, Computer science, Computer Science - Artificial Intelligence, Credit card fraud, Machine Learning (stat.ML), 02 engineering and technology, computer.software_genre, Random forest, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Credit card, 020901 industrial engineering & automation, Artificial Intelligence (cs.AI), ComputingMethodologies_PATTERNRECOGNITION, Order (business), Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), 020201 artificial intelligence & image processing, Data mining, computer
Abstract: Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However purchase behaviour and fraudster strategies may change over time. This phenomenon is named dataset shift or concept drift in the domain of fraud detection. In this paper, we present a method to quantify day-by-day the dataset shift in our face-to-face credit card transactions dataset (card holder located in the shop) . In practice, we classify the days against each other and measure the efficiency of the classification. The more efficient the classification, the more different the buying behaviour between two days, and vice versa. Therefore, we obtain a distance matrix characterizing the dataset shift. After an agglomerative clustering of the distance matrix, we observe that the dataset shift pattern matches the calendar events for this time period (holidays, week-ends, etc). We then incorporate this dataset shift knowledge in the credit card fraud detection task as a new feature. This leads to a small improvement of the detection., Presented at IEEE Artificial Intelligence and Knowledge Engineering (AIKE 2019)
Published: 2019

29. Formal Methods for Mobile Robots

Author: Maria Potop-Butucaru, Xavier Urbain, Sébastien Tixeuil, Nathalie Sznajder, Networks and Performance Analysis (NPA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Modélisation et Vérification (MoVe), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Paola Flocchini, Giuseppe Prencipe, and Nicola Santoro
Subjects: Model checking, Correctness, Computer science, 0102 computer and information sciences, 02 engineering and technology, Mathematical proof, computer.software_genre, 01 natural sciences, 0202 electrical engineering, electronic engineering, information engineering, Mobile robots, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Proof assistant, Proof certification, Programming language, Formal methods, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], 020207 software engineering, Mobile robot, Program synthesis, 010201 computation theory & mathematics, Distributed algorithm, Distributed algorithms, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], computer
Abstract: International audience; Most existing work in the literature typically ensures the correctness of mobile robot protocols via ad hoc handwritten proofs, which are both cumbersome and error-prone.This paper surveys state-of-the-art results about applying formal methods approaches (namely, model-checking, program synthesis, and proof assistants) to the context of mobile robot networks. Those methods already proved useful for bug-hunting in published literature, designing correct-by-design optimal protocols, and certifying impossibility results and protocols.
Published: 2019

30. DEvIR: Data Collection and Analysis for the Recommendation of Events and Itineraries

Author: Jérôme Gensel, Diana Nurbakova, Sylvie Calabretto, Léa Laporte, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Spatio-temporal information systems (STEAMER ), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Nurbakova, Diana
Subjects: 021103 operations research, Information retrieval, Data collection, Computer science, business.industry, Event (computing), 0211 other engineering and technologies, Context (language use), Usability, 02 engineering and technology, 020204 information systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], business
Abstract: International audience; Distributed events such as multi-day festivals and conventions attract thousands of attendees. Their programs are usually very dense, which makes it difficult for users to select activities to perform. Recent works have proposed event and itinerary recommendation algorithms to solve this problem. Although several datasets have been made available for the evaluation of event recommendation algorithms, they do not suit well for the case of distributed events or itinerary recommendation. Based on the study of available online resources, we define dataset attributes required to perform event and itinerary recommendations in the context of distributed events, and discuss the compliance of existing datasets to these requirements. Revealing the lack of publicly available datasets with desired features, we describe a data collection process to acquire the publicly available data from a major comic book convention website. We present the characteristics of the collected data and discuss its usability for evaluating recommendation algorithms.
Published: 2019

31. Multiple perspectives HMM-based feature engineering for credit card fraud detection

Author: Michael Granitzer, Léa Laporte, Liyun He-Guelton, Olivier Caelen, Yvan Lucas, Pierre-Edouard Portier, Sylvie Calabretto, Portier, Pierre-Edouard, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Atos Worldline, Atos, and University of Passau
Subjects: FOS: Computer and information sciences, Feature engineering, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer science, Computer Science - Artificial Intelligence, Machine Learning (stat.ML), 02 engineering and technology, computer.software_genre, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), Statistics - Machine Learning, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, ComputingMilieux_MISCELLANEOUS, Credit card fraud, 020207 software engineering, 16. Peace & justice, Payment terminal, Random forest, Credit card, Artificial Intelligence (cs.AI), ComputingMethodologies_PATTERNRECOGNITION, Data mining, Cryptography and Security (cs.CR), Database transaction, computer
Abstract: Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection., Presented as a poster in the conference SAC 2019: 34th ACM/SIGAPP Symposium on Applied Computing in April 2019
Published: 2019

32. Continuous vs. Discrete Asynchronous Moves: A Certified Approach for Mobile Robots

Author: Robin Pelle, Lionel Rieg, Pierre Courtieu, Xavier Urbain, Thibaut Balabonski, Sébastien Tixeuil, Université Paris-Saclay, Vérification d'Algorithmes, Langages et Systèmes (LRI) (VALS - LRI), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), CEDRIC. Systèmes sûrs (CEDRIC - SYS), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Department of Computer Science (YALE), Yale University [New Haven], Networks and Performance Analysis (NPA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratory of Information, Network and Communication Sciences (LINCS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom [Paris] (IMT), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6, LIP6, F-75005 Paris, France, Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), VERIMAG (VERIMAG - IMAG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Mohamed Faouzi Atig, and Alexander A. Schwarzmann
Subjects: Theoretical computer science, Computer science, ACM: F.: Theory of Computation/F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], Formal Proof, Proof Assistant, 01 natural sciences, Formal proof, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, Computer Science::Robotics, [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS, 0202 electrical engineering, electronic engineering, information engineering, Coq, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], ComputingMilieux_MISCELLANEOUS, Program Verification, Continuous modelling, Mobile Autonomous Robots, Proof assistant, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Mobile robot, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS, Distributed Algorithms, ACM: D.: Software/D.1: PROGRAMMING TECHNIQUES, Automated theorem proving, 010201 computation theory & mathematics, Asynchronous communication, Distributed algorithm, Automated Deduction, Robot, 020201 artificial intelligence & image processing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: Oblivious Mobile Robots have been studied both in continuous Euclidean spaces, and discrete spaces (that is, graphs). However the obtained literature forms distinct sets of results for the two settings. In our view, the continuous model reflects well the physicality of robots operating in some real environment, while the discrete model reflects well the digital nature of autonomous robots, whose sensors and computing capabilities are inherently finite.
Published: 2019

33. Recommendation of activity sequences during distributed events

Author: Diana Nurbakova, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Distribution, Recherche d'Information et Mobilité (DRIM), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon, Sylvie Calabretto, INSA Lyon, LIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/École Centrale de Lyon, Ecole doctorale InfoMaths (ED 512), Jérôme Gensel, and Léa Laporte
Subjects: Leisure activities, Evènements distribués, Collection de données, Computer science, Cruise, Internet privacy, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], event recommendation, Context (language use), User satisfaction, Satisfaction de l'utilisateur, 02 engineering and technology, Recommender system, users psychological profiles, Construction d'itinéraire, Exhibition, personalisation, Satisfaction utilisateur, 020204 information systems, Recommender systems, 0202 electrical engineering, electronic engineering, information engineering, Decision-making, itinerary recom-mendation, Recommandation de séquences, Systèmes de recommandaton, business.industry, Itinerary construction, Activités de loisirs, CCS CONCEPTS • Information systems → Personalization, Informatique, Système de recommandation, Sequence-aware recommender systems, User satifaction, Distributed events, Order (business), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Data collection, TRIPS architecture, Recommandation system, 020201 artificial intelligence & image processing, business, Information Technology
Abstract: Multi-day events such as conventions, festivals, cruise trips, to which we refer to as distributed events, have become very popular in recent years, attracting hundreds or thousands of participants. Their programs are usually very dense, making it challenging for the attendees to make a decision which events to join. Recommender systems appear as a common solution in such an environment. While many existing solutions deal with personalised recommendation of single items, recent research focuses on the recommendation of consecutive items that exploits user's behavioural patterns and relations between entities, and handles geographical and temporal constraints. In this thesis, we first formulate the problem of recommendation of activity sequences, classify and discuss the types of influence that have an impact on the estimation of the user's interest in items. Second, we propose an approach (ANASTASIA) to solve this problem, which aims at providing an integrated support for users to create a personalised itinerary of activities. ANASTASIA brings together three components, namely: (1) estimation of the user’s interest in single items, (2) use of sequential influence on activity performance, and (3) building of an itinerary that takes into account spatio-temporal constraints. Thus, the proposed solution makes use of the methods based on sequence learning and discrete optimisation. Moreover, stating the lack of publicly available datasets that could be used for the evaluation of event and itinerary recommendation algorithms, we have created two datasets, namely: (1) event attendance on board of a cruise (Fantasy_db) based on a conducted user study, and (2) event attendance at a major comic book convention (DEvIR). This allows to perform evaluation of recommendation methods, and contributes to the reproducibility of results.; Les événements distribués, se déroulant sur plusieurs jours et/ou sur plusieurs lieux, tels que les conventions, festivals ou croisières, sont de plus en plus populaires ces dernières années et attirant des milliers de participants. Les programmes de ces événements sont généralement très denses, avec un grand nombre d'activités se déroulant en parallèle. Ainsi, choisir les activités à entreprendre est devenu un véritable défi pour les participants. Les systèmes de recommandation peuvent constituer une solution privilégiée dans ce genre d'environnement. De nombreux travaux en recommandation se sont concentrés sur la recommandation personnalisée d'objets spatiaux (points d'intérêts immuables dans le temps ou événements éphémères) indépendants les uns des autres. Récemment, la communauté scientifique s'est intéressée à la recommandation de séquences de points d'intérêts, exploitant des motifs comportementaux des utilisateurs et incorporant des contraintes spatio-temporelles pour recommander un itinéraire de points d'intérêts. Néanmoins, très peu de travaux se sont intéressés à la problématique de la recommandation de séquence d'activités, problème plus difficile du fait du caractère éphémère des objets à recommander. Dans cette thèse, nous proposons tout d'abord une formalisation du problème de la recommandation de séquences d'activités. Dans ce cadre, nous proposons et discutons une classification des types d'influences pouvant avoir un impact sur l'estimation de l'intérêt des utilisateurs dans les activités. Ensuite, nous proposons ANASTASIA, une approche de recommandation personnalisée de séquences d'activités lors des événements distribués. Notre approche est basée sur trois composants clés : (1) l'estimation de l'intérêt d'un utilisateur pour une activité, prenant en compte différentes influences, (2) l'intégration de motifs comportementaux d'utilisateurs basés sur leurs historiques d'activités et (3) la construction d'un planning ou séquence d'activités prenant en compte les contraintes spatio-temporelles de l'utilisateur et des activités. Nous explorons ainsi des méthodes issus de l'apprentissage de séquences et de l'optimisation discrète pour résoudre le problème. Enfin, nous démontrons le manque de jeu de données librement accessibles pour l'évaluation des algorithmes de recommandation d'événements et de séquences d'événements. Nous pallions à ce problème en proposant deux jeux de données, librement accessibles, que nous avons construits au cours de la thèse: Fantasy_db et DEvIR. Fantasy_db comporte des données de participation à des événements lors d'une croisière, recueillies lors d'une étude utilisateur, tandis que DEvIR réunit des données de participation au Comic Con de San Diego, convention majeure dans le domaine.
Published: 2018

34. EActors: fast and flexible trusted computing using SGX

Author: Rüdiger Kapitza, Gaël Thomas, Sonia Ben Mokhtar, Stefan Brenner, Sara Bouchenak, Vasily A. Sartakov, Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig], Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Algorithmes, Composants, Modèles Et Services pour l'informatique répartie (ACMES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Département Informatique (INF), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: business.industry, Computer science, Software development, 020206 networking & telecommunications, Usability, 02 engineering and technology, Trusted Computing, computer.software_genre, Encryption, Actors, Software, Privacy, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, User space, Operating system, Intel SGX, Use case, Central processing unit, Trusted execution, [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS], business, computer, SGX
Abstract: International audience; Novel trusted execution support, as offered by Intel's Software Guard eXtensions (SGX), embeds seamlessly into user space applications by establishing regions of encrypted memory, called enclaves. Enclaves comprise code and data that is exe- cuted under special protection of the CPU and can only be accessed via an enclave defined interface. To facilitate the usability of this new system abstraction, Intel offers a soft- ware development kit (SGX SDK). While the SDK eases the use of SGX, it misses appropriate programming support for inter-enclave interaction, and demands to hardcode the exact use of trusted execution into applications, which restricts flexibility. This paper proposes EActors, an actor framework that is tailored to SGX and offers a more seamless, flexible and efficient use of trusted execution - especially for applica- tions demanding multiple enclaves. EActors disentangles the interaction with enclaves and, among them, from costly exe- cution mode transitions. It features lightweight fine-grained parallelism based on the concept of actors, thereby avoid- ing costly SGX SDK provided synchronisation constructs. Finally, EActors offers a high degree of freedom to execute actors, either untrusted or trusted, depending on security requirements and performance demands. We implemented two use cases on top of EActors: (i) a secure instant messag- ing service, and (ii) a secure multi-party computation service. Both illustrate the ability of EActors to seamlessly and ef- fectively build secure applications. Furthermore, our perfor- mance evaluation results show that securing the messaging service with EActors improves performance compared to the vanilla versions of JabberD2 and ejabberd by up to 40×
Published: 2018

35. Brief Announcement Continuous vs. Discrete Asynchronous Moves: A Certified Approach for Mobile Robots

Author: Pierre Courtieu, Lionel Rieg, Sébastien Tixeuil, Xavier Urbain, Robin Pelle, Thibaut Balabonski, Vérification d'Algorithmes, Langages et Systèmes (LRI) (VALS - LRI), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), CEDRIC. Systèmes sûrs (CEDRIC - SYS), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Department of Computer Science (YALE), Yale University [New Haven], Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Networks and Performance Analysis (NPA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Xavier Defago, Toshimitsu Masuzawa, Koichi Wada, and Taisuke Izumi, Petr Kuznetsov
Subjects: Computer science, business.industry, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Mobile robot, 0102 computer and information sciences, 02 engineering and technology, Certification, 01 natural sciences, Computer Science::Robotics, 010201 computation theory & mathematics, Asynchronous communication, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Computer network
Abstract: International audience; We explore the possibility of establishing a first bridge between the continuous movements and observation vs. discrete movements and observation in the context of autonomous mobile robots.Our position is that the continuous model reflects well the physicality of robots operating in some environment, while the discrete model reflects well the digital nature of autonomous robots, whose sensors and computing capabilities are inherently finite.For this purpose, we consider that robots make continuous, non atomic moves, but only sense in a discrete manner the position of robots.Our approach is certified using the Coq proof assistant and the Pactole framework.
Published: 2018

36. Géo-localisation basée sur des connaissances d'images annotées

Author: Elöd Egyed-Zsigmond, Harald Kosch, Victor Charpenay, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Faculty of Informatics and Mathematics (FMI), and Fakultät für Informatik und Mathematik
Subjects: multimedia, 0209 industrial biotechnology, Information retrieval, Computer science, linked data, 02 engineering and technology, Linked data, Library and Information Sciences, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Geotagging, semantic web, 020901 industrial engineering & automation, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, information retrieval, Humanities, Semantic Web
Abstract: International audience; Currently, Reverse Geo-tagging relies on the keywords describing an image and use probabilistic algorithmsto guess the localization of the depicted scene. However, such algorithms still perform poorly and show clear limitations.Notably, the location estimation only occurs at the landmark level; regions or countries are only processed throughtheir centroid.In this paper, we address this particular issue by exploring a semantic approach, which identifies geographical entities among the keywordsto localize the picture (being a landmark or a country). We leverage components of the Linked Open Data cloud to find possible entities. The benefits of our approach, as opposed to numerical approaches, include an in-depth study of the ``geo-relevance'' of an image; Actuellement, la géo-localisation d’une image consiste à appliquer des algorithmesprobabilistes sur les mots-clés la décrivant pour estimer la position de la scène qu’elle représente.Cependant, de tels algorithmes montrent des limites clairement identifiables. En particulier,l’estimation se fait toujours à l’échelle d’un point, les régions et pays étant réduits à leurbarycentre. Dans cet article, nous nous concentrons sur ce problème en explorant une méthodesémantique qui identifie des entités géographique (issues du Linked Open Data) pour localiserune photo (qu’il s’agisse d’un point sur une carte ou un pays). L’avantage d’une telle approchevis-à-vis des méthodes numériques est notamment la possibilité d’étudier la pertinence géographiqued’une image.
Published: 2016

37. Intrusion Detection Using Mouse Dynamics

Author: Elöd Egyed-Zsigmond, Margit Antal, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Biometrics, Computer science, Feature extraction, 0211 other engineering and technologies, Computer Science - Human-Computer Interaction, 02 engineering and technology, Intrusion detection system, Drag and drop, Machine learning, computer.software_genre, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], 0202 electrical engineering, electronic engineering, information engineering, 021110 strategic, defence & security studies, business.industry, Data set, Signal Processing, Pattern recognition (psychology), Benchmark (computing), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Raw data, Cryptography and Security (cs.CR), computer, Software
Abstract: Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition, which against the few subjects, can be considered the first adequate publicly available one. This paper presents a performance evaluation study on this data set for impostor detection. The existence of very short test sessions makes this data set challenging. Raw data were segmented into mouse move, point and click and drag and drop types of mouse actions, then several features were extracted. In contrast to keystroke dynamics, mouse data is not sensitive, therefore it is possible to collect negative mouse dynamics data and to use two-class classifiers for impostor detection. Both action- and set of actions-based evaluations were performed. Set of actions-based evaluation achieves 0.92 AUC on the test part of the data set. However, the same type of evaluation conducted on the training part of the data set resulted in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to be the best actions for impostor detection., Submitted to IET Biometrics on 23 May 2018
Published: 2018

38. Héron: Taming Tail Latencies in Key-Value Stores under Heterogeneous Workloads

Author: Vikas Jaiman, Etienne Rivière, Vivien Quéma, Sonia Ben Mokhtar, Lydia Y. Chen, Université Catholique de Louvain = Catholic University of Louvain (UCL), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Efficient and Robust Distributed Systems (ERODS ), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), IBM Research Laboratory [Zurich], IBM Research [Zurich], Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and UCL - SST/ICTM/INGI - Pôle en ingénierie informatique
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], biology, Computer science, business.industry, Scheduling, Replica, Performance, 02 engineering and technology, Dynamic priority scheduling, Scheduling (computing), [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], 020204 information systems, Server, biology.animal, Distributed data store, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Latency (engineering), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Heron, Distributed Storage, Selection algorithm, Computer network
Abstract: International audience; Avoiding latency variability in distributed storage systems is challenging. Even in well-provisioned systems, factors such as the contention on shared resources or the unbalanced load between servers affect the latencies of requests and in particular the tail (95th and 99th percentile) of their distribution. One effective counter measure for reducing tail latency in key-value stores is to provide efficient replica selection algorithms. However, existing solutions are based on the assumption that all requests have almost the same execution time. This is not true for real workloads. This mismatch leads to increased latencies for requests with short execution time that get scheduled behind requests with large execution times. We propose Héron, a replica selection algorithm that supports workloads with heterogeneous request execution times. We evaluate Héron in a cluster of machines using a synthetic dataset inspired from the Facebook dataset as well as two real datasets from Flickr and WikiMedia. Our results show that Héron outperforms state-of-the-art algorithms by reducing both median and tail latency by up to 41%.
Published: 2018

39. HMC: Robust Privacy Protection of Mobility Data against Multiple Re-Identification Attacks

Author: Sonia Ben Mokhtar, Mohamed Maouche, Sara Bouchenak, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Bouchenak, Sara
Subjects: [INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], Information privacy, Computer Networks and Communications, Computer science, Protection Mechanism, 02 engineering and technology, Computer security, computer.software_genre, Re identification, Mobility Data, Utility, [INFO.INFO-PF] Computer Science [cs]/Performance [cs.PF], [INFO.INFO-ET] Computer Science [cs]/Emerging Technologies [cs.ET], 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], End user, Privacy protection, 020206 networking & telecommunications, 020207 software engineering, Human-Computer Interaction, Information sensitivity, Re-identification Attack, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Hardware and Architecture, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Location Privacy, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Mobile device, computer, Protection mechanism
Abstract: International audience; With the wide propagation of handheld devices, more and more mobile sensors are being used by end users on a daily basis. Those sensors could be leveraged to gather useful mobility data for city planners, business analysts and researches. However, gathering and exploiting mobility data raises many privacy threats. Sensitive information such as one's home or work place, hobbies, religious beliefs, political or sexual preferences can be inferred from the gathered data. In the last decade, Location Privacy Protection Mechanisms (LPPMs) have been proposed to protect user data privacy. However existing LPPMs fail at effectively protecting the users as most of them reason on local mobility features: micro-mobility (e.g., individual geographical coordinates) while ignoring higher level mobility features, which may allow attackers to discriminate between users. In this paper we propose H MC the first LPPM that reasons on the overall user mobility abstracted using heat maps. We evaluate H MC using four real mobility traces and multiple privacy and utility metrics. The results show that with H MC, across all the datasets 87% of mobile users are successfully protected against re-identification attacks, while others LPPMs only achieve a protection ranging from 43% to 79%. By considering only users protected with a high utility, the proportion of users stays high for H MC with 75%, while for others LPPMs it goes down to proportions between 4% and 43%.
Published: 2018

40. Critical Analysis of LPL according to Articles 12 - 14 of the GDPR

Author: Armin Gerl, Dirk Pohl, Distributed and Multimedia Information Systems (DIMIS), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Universität Passau [Passau]
Subjects: Computer science, business.industry, Privacy policy, 05 social sciences, Internet privacy, 02 engineering and technology, 16. Peace & justice, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], General Data Protection Regulation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], 0509 other social sciences, 050904 information & library sciences, business, ComputingMilieux_MISCELLANEOUS
Abstract: On the 25th May 2018 the General Data Protection Regulation (GDPR) will enter into force implying new challenges to both legal and computer sciences. The Layered Privacy Language (LPL) is intended to model privacy policies to enforce policy-based, privacy-preserving processing. In this paper, we identify requirements for privacy policies based on Art. 12 - 14 of the GDPR, analyze LPL according to the derived requirements, and propose improvements for LPL accordingly.
Published: 2018

41. A Control-Theoretic Approach for Location Privacy in Mobile Applications

Author: Sara Bouchenak, Bogdan Robu, Nicolas Marchand, Sophie Cerf, Sonia Ben Mokhtar, GIPSA - Systèmes non linéaires et complexité (GIPSA-SYSCO), Département Automatique (GIPSA-DA), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: Focus (computing), Information privacy, Service (systems architecture), Computer science, media_common.quotation_subject, Distributed computing, 020206 networking & telecommunications, 02 engineering and technology, Data modeling, System dynamics, [SPI.AUTO]Engineering Sciences [physics]/Automatic, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-MC]Computer Science [cs]/Mobile Computing, Control theory, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], 020201 artificial intelligence & image processing, Relevance (information retrieval), Quality (business), media_common
Abstract: International audience; The prevalent use of mobile applications using location information to improve the quality of their service has arisen privacy issues, particularly regarding the extraction of user's points on interest. Many studies in the literature focus on presenting algorithms that allow to protect the user of such applications. However, these solutions often require a high level of expertise to be understood and tuned properly. In this paper, the first control-based approach of this problem is presented. The protection algorithm is considered as the " physical " plant and its parameters as control signals that enable to guarantee privacy despite user's mobility pattern. The following of the paper presents the first control formulation of POI-related privacy measure, as well as dynamic modeling and a simple yet efficient PI control strategy. The evaluation using simulated mobility records shows the relevance and efficiency of the presented approach.
Published: 2018

42. LPL, Towards a GDPR-Compliant Privacy Language: Formal Definition and Usage

Author: Lionel Brunie, Nadia Bennani, Armin Gerl, Harald Kosch, Distributed and Multimedia Information Systems (DIMIS), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Springer, Berlin, and Heidelberg
Subjects: Computer science, 02 engineering and technology, Privacy management, Computer security, computer.software_genre, Privacy model, Set (abstract data type), [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], 020204 information systems, General Data Protection Regulation, Retention Management, 0202 electrical engineering, electronic engineering, information engineering, Information system, 020201 artificial intelligence & image processing, computer, Formal description, ComputingMilieux_MISCELLANEOUS
Abstract: The upcoming General Data Protection Regulation (GDPR) imposes several new legal requirements for privacy management in information systems. In this paper, we introduce LPL, an extensible Layered Privacy Language that allows to express and enforce these new privacy properties such as personal privacy, user consent, data provenance, and retention management. We present a formal description of LPL. Based on a set of usage examples, we present how LPL expresses and enforces the main features of the GDPR and application of state-of-the-art anonymization techniques.
Published: 2018

43. CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions

Author: Marcelo Pasin, Rüdiger Kapitza, David Goltzsche, Rafael Pires, Sara Bouchenak, Antoine Boutet, Sonia Ben Mokhtar, Valerio Schiavoni, Pascal Felber, Institut d'Informatique [Neuchâtel] (IIUN), Université de Neuchâtel (UNINE), Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig], Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), LaSIGE [Lisboa], Universidade de Lisboa (ULISBOA)-Faculdade de Ciências, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), Universidade de Lisboa = University of Lisbon (ULISBOA)-Faculdade de Ciências, and ANR-17-CE25-0017,PRIMaTE,Préservation de la vie privée dans un environnement d'exécution multi-enclaves fiables(2017)
Subjects: FOS: Computer and information sciences, Information retrieval, Computer Science - Cryptography and Security, biology, Computer science, [INFO.INFO-WB]Computer Science [cs]/Web, Cyclosa, 020206 networking & telecommunications, 02 engineering and technology, biology.organism_classification, Search engine, Information sensitivity, Computer Science - Distributed, Parallel, and Cluster Computing, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], Scalability, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Cryptography and Security (cs.CR)
Abstract: International audience; By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and accurate private Web search solution. CYCLOSA improves security by relying on trusted execution environments (TEEs) as provided by Intel SGX. Further, CYCLOSA proposes a novel adaptive privacy protection solution that reduces the risk of user re-identification. CYCLOSA sends fake queries to the search engine and dynamically adapts their count according to the sensitivity of the user query. In addition, CYCLOSA meets scalability as it is fully decentralized, spreading the load for distributing fake queries among other nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real query and the fake queries separately, in contrast to other existing solutions that mix fake and real query results.
Published: 2018

44. ACCIO: How to Make Location Privacy Experimentation Open and Easy

Author: Mohamed Maouche, Antoine Boutet, Vincent Primault, Lionel Brunie, Sonia Ben Mokhtar, Sara Bouchenak, Computer science department [University College London] (UCL-CS), University College of London [London] (UCL), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Computer science [University College of London] (UCL-CS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Information privacy, Computer science, 0202 electrical engineering, electronic engineering, information engineering, 020207 software engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, Dissemination, Data science, Task (project management)
Abstract: International audience; The advent of mobile applications collecting and exploiting the location of users opens a number of privacy threats. To mitigate these privacy issues, several protection mechanisms have been proposed this last decade to protect users' location privacy. However, these protection mechanisms are usually implemented and evaluated in monolithic way, with heterogeneous tools and languages. Moreover, they are evaluated using different methodologies, metrics and datasets. This lack of standard makes the task of evaluating and comparing protection mechanisms particularly hard. In this paper, we present ACCIO, a unified framework to ease the design and evaluation of protection mechanisms. Thanks to its Domain Specific Language, ACCIO allows researchers and practitioners to define and deploy experiments in an intuitive way, as well as to easily collect and analyse the results. ACCIO already comes with several state-of-the-art protection mechanisms and a toolbox to manipulate mobility data. Finally, ACCIO is open and easily extensible with new evaluation metrics and protection mechanisms. This openness, combined with a description of experiments through a user-friendly DSL, makes ACCIO an appealing tool to reproduce and disseminate research results easier. In this paper, we present ACCIO's motivation and architecture, and demonstrate its capabilities through several use cases involving multiples metrics, state-of-the-art protection mechanisms, and two real-life mobility datasets collected in Beijing and in the San Francisco area.
Published: 2018

45. Towards Dynamic End-to-End Privacy Preserving Data Classification

Author: Rania Talbi, Sara Bouchenak, Lydia Y. Chen, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), IBM Zurich Research Laboratory, and IBM
Subjects: 021110 strategic, defence & security studies, Information privacy, Incremental decision tree, Information retrieval, business.industry, Computer science, Data classification, 0211 other engineering and technologies, Decision tree, Cryptography, 02 engineering and technology, Encryption, Data modeling, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], End-to-end principle, business, ComputingMilieux_MISCELLANEOUS
Abstract: In this paper we present DAPPLE, a standalone End-to-End privacy preserving data classification service. It allows incremental decision tree learning over encrypted training data continuously sent by multiple data owners, without having access to the actual content of this data. In the same time, the learnt classification model is used to respond to encrypted classification queries while preserving the privacy of the query, the output corresponding to it and the model itself.
Published: 2018

46. Dynamic Modeling of Location Privacy Protection Mechanisms

Author: Nicolas Marchand, Sara Bouchenak, Sophie Cerf, Sonia Ben Mokhtar, Bogdan Robu, GIPSA - Systèmes non linéaires et complexité (GIPSA-SYSCO), Département Automatique (GIPSA-DA), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université de Lyon-Institut National des Sciences Appliquées (INSA), Silvia Bonomi, and Etienne Rivière
Subjects: Service (systems architecture), Points of interest, Location privacy, Point of interest, Computer science, Location Based Services, Modeling, 020206 networking & telecommunications, 02 engineering and technology, Computer security, computer.software_genre, Control of computing systems, System dynamics, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Ask price, 020204 information systems, Metric (mathematics), Location-based service, 0202 electrical engineering, electronic engineering, information engineering, Relevance (information retrieval), [INFO]Computer Science [cs], Mobile device, computer
Abstract: International audience; Mobile applications tend to ask for users’ location in order to improve the service they provide. However, aside from increasing their service utility, they may also store these data, analyze them or share them with external parties. These privacy threats for users are a hot topic of research, leading to the development of so called Location Privacy Protection Mechanisms. LPPMs often are configurable algorithms that enable the tuning of the privacy protection they provide and thus the leveraging of the service utility. However, they usually do not provide ways to measure the achieved privacy in practice for all users of mobile devices, and even less clues on how a given configuration will impact privacy of the data given the specificities of everyone’s mobility. Moreover, as most Location Based Services require the user position in real time, these measures and predictions should be achieved in real time. In this paper we present a metric to evaluate privacy of obfuscated data based on users’ points of interest as well as a predictive model of the impact of a LPPM on these measure; both working in a real time fashion. The evaluation of the paper’s contributions is done using the state of the art LPPM Geo-I on synthetic mobility data generated to be representative of real-life users’ movements. Results highlight the relevance of the metric to capture privacy, the fitting of the model to experimental data, and the feasibility of the on-line mechanisms due to their low computing complexity.
Published: 2018

47. Can Adaptive Feedforward Control Improve Operation of Cloud Services?

Author: Ioan Doré Landau, Bogdan Robu, Nicolas March, Sophie Cerf, Jaime Saavedra, Sara Bouchenak, GIPSA - Systèmes linéaires et robustesse (GIPSA-SLR), Département Automatique (GIPSA-DA), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Systèmes non linéaires et complexité (GIPSA-SYSCO), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: Adaptive control, business.industry, Computer science, Feedback control, Feed forward, Control engineering, Cloud computing, 02 engineering and technology, [INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, 020204 information systems, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, business, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2018

48. X-Search: Revisiting Private Web Search using Intel SGX

Author: Antoine Boutet, Sonia Ben Mokhtar, Valerio Schiavoni, Rafael Pires, Pascal Felber, Marcelo Pasin, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon, CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria), Institut d'Informatique [Neuchâtel] (IIUN), Université de Neuchâtel (UNINE), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Grenoble - Rhône-Alpes
Subjects: FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer science, Cryptography, Throughput, 02 engineering and technology, security, Computer security, computer.software_genre, privacy, Search engine, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Software, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Private information retrieval, Middleware, Guard (information security), business.industry, 020206 networking & telecommunications, Competitor analysis, web search, Computer Science - Distributed, Parallel, and Cluster Computing, Middleware (distributed applications), Distributed, Parallel, and Cluster Computing (cs.DC), business, computer, Cryptography and Security (cs.CR), SGX
Abstract: The exploitation of user search queries by search engines is at the heart of their economic model. As consequence, offering private Web search functionalities is essential to the users who care about their privacy. Nowadays, there exists no satisfactory approach to enable users to access search engines in a privacy-preserving way. Existing solutions are either too costly due to the heavy use of cryptographic mechanisms (e.g., private information retrieval protocols), subject to attacks (e.g., Tor, TrackMeNot, GooPIR) or rely on weak adversarial models (e.g., PEAS). This paper introduces X-Search , a novel private Web search mechanism building on the disruptive Software Guard Extensions (SGX) proposed by Intel. We compare X-Search to its closest competitors, Tor and PEAS, using a dataset of real web search queries. Our evaluation shows that: (1) X-Search offers stronger privacy guarantees than its competitors as it operates under a stronger adversarial model; (2) it better resists state-of-the-art re-identification attacks; and (3) from the performance perspective, X-Search outperforms its competitors both in terms of latency and throughput by orders of magnitude., Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference. Las Vegas, NV, USA, December 11-15, 2017, 11 pages
Published: 2018

49. TournaRank: when retrieval becomes document competition

Author: Léa Laporte, Ronan Tournier, Gilles Hubert, Yoann Pitarch, Karen Pinel-Sauvagnat, Recherche d’Information et Synthèse d’Information (IRIT-IRIS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Systèmes d’Informations Généralisées (IRIT-SIG), Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National des Sciences Appliquées de Lyon - INSA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Ecole Centrale de Lyon (FRANCE), Université Lumière-Lyon 2 (FRANCE), Université Jean Moulin Lyon 3 (FRANCE), and Institut National Polytechnique de Toulouse - INPT (FRANCE)
Subjects: Information retrieval, Computer science, 05 social sciences, Recherche d'information, 02 engineering and technology, Library and Information Sciences, Management Science and Operations Research, Computer Science Applications, Homogeneous, 020204 information systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], IR Model- Feature-based representation -Tournament, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Leverage (statistics), Tournament, Learning to rank, 0509 other social sciences, 050904 information & library sciences, Feature set, Information Systems
Abstract: International audience; Numerous feature-based models have been recently proposed by the information retrieval community. The capability of features to express different relevance facets (query- or document-dependent) can explain such a success story. Such models are most of the time supervised, thus requiring a learning phase. To leverage the advantages of feature-based representations of documents, we propose TournaRank, an unsupervised approach inspired by real-life game and sport competition principles. Documents compete against each other in tournaments using features as evidences of relevance. Tournaments are modeled as a sequence of matches, which involve pairs of documents playing in turn their features. Once a tournament is ended, documents are ranked according to their number of won matches during the tournament. This principle is generic since it can be applied to any collection type. It also provides great flexibility since different alternatives can be considered by changing the tournament type, the match rules, the feature set, or the strategies adopted by documents during matches. TournaRank was experimented on several collections to evaluate our model in different contexts and to compare it with related approaches such as Learning To Rank and fusion ones: the TREC Robust2004 collection for homogeneous documents, the TREC Web2014 (ClueWeb12) collection for heterogeneous web documents, and the LETOR3.0 collection for comparison with supervised feature-based models.
Published: 2018

50. Sequence Classification for Credit-Card Fraud Detection

Author: Michael Granitzer, Pierre-Edouard Portier, Sylvie Calabretto, Olivier Caelen, Johannes Jurgovsky, Konstantin Ziegler, Liyun He-Guelton, University of Passau, Distribution, Recherche d'Information et Mobilité (DRIM), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Know-Center (AUSTRIA), Know-Center Graz, Atos Worldline, and Atos
Subjects: business.industry, Computer science, media_common.quotation_subject, Credit card fraud, General Engineering, 02 engineering and technology, Service provider, Machine learning, computer.software_genre, Payment, Computer Science Applications, Random forest, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Artificial intelligence, business, Classifier (UML), computer, ComputingMilieux_MISCELLANEOUS, media_common
Abstract: Due to the growing volume of electronic payments, the monetary strain of credit-card fraud is turning into a substantial challenge for financial institutions and service providers, thus forcing them to continuously improve their fraud detection systems. However, modern data-driven and learning-based methods, despite their popularity in other domains, only slowly find their way into business applications. In this paper, we phrase the fraud detection problem as a sequence classification task and employ Long Short-Term Memory (LSTM) networks to incorporate transaction sequences. We also integrate state-of-the-art feature aggregation strategies and report our results by means of traditional retrieval metrics. A comparison to a baseline random forest (RF) classifier showed that the LSTM improves detection accuracy on offline transactions where the card-holder is physically present at a merchant. Both the sequential and non-sequential learning approaches benefit strongly from manual feature aggregation strategies. A subsequent analysis of true positives revealed that both approaches tend to detect different frauds, which suggests a combination of the two. We conclude our study with a discussion on both practical and scientific challenges that remain unsolved.
Published: 2018

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