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4. On the relevance of APIs facing fairwashed audits

Author

Bourrée, Jade Garcia, Merrer, Erwan Le, Tredan, Gilles, and Rottembourg, Benoît

Subjects
Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computers and Society, Computer Science - Software Engineering, Computers and Society (cs.CY), Machine Learning (cs.LG)
Abstract

Recent legislation required AI platforms to provide APIs for regulators to assess their compliance with the law. Research has nevertheless shown that platforms can manipulate their API answers through fairwashing. Facing this threat for reliable auditing, this paper studies the benefits of the joint use of platform scraping and of APIs. In this setup, we elaborate on the use of scraping to detect manipulated answers: since fairwashing only manipulates API answers, exploiting scraps may reveal a manipulation. To abstract the wide range of specific API-scrap situations, we introduce a notion of proxy that captures the consistency an auditor might expect between both data sources. If the regulator has a good proxy of the consistency, then she can easily detect manipulation and even bypass the API to conduct her audit. On the other hand, without a good proxy, relying on the API is necessary, and the auditor cannot defend against fairwashing. We then simulate practical scenarios in which the auditor may mostly rely on the API to conveniently conduct the audit task, while maintaining her chances to detect a potential manipulation. To highlight the tension between the audit task and the API fairwashing detection task, we identify Pareto-optimal strategies in a practical audit scenario. We believe this research sets the stage for reliable audits in practical and manipulation-prone setups., 18 pages, 7 figures

Published
2023

8. On the Feasibility of Perfect Resilience with Local Fast Failover

Author

Foerster, Klaus-Tycho, Hirvonen, Juho, Pignolet, Yvonne-Anne, Schmid, Stefan, Tredan, Gilles, University of Vienna [Vienna], Department of Information and Computer Science [Aalto], Helsinki Institute for Information Technology (HIIT), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University, DFINITY Foundation [Zurich], Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique (LAAS-TSF), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-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)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Aalto University-University of Helsinki-Aalto University-University of Helsinki, Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, and TREDAN, Gilles

Subjects
Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Computer Science - Distributed, Parallel, and Cluster Computing, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Distributed, Parallel, and Cluster Computing (cs.DC)
Abstract

In order to provide a high resilience and to react quickly to link failures, modern computer networks support fully decentralized flow rerouting, also known as local fast failover. In a nutshell, the task of a local fast failover algorithm is to pre-define fast failover rules for each node using locally available information only. These rules determine for each incoming link from which a packet may arrive and the set of local link failures (i.e., the failed links incident to a node), on which outgoing link a packet should be forwarded. Ideally, such a local fast failover algorithm provides a perfect resilience deterministically: a packet emitted from any source can reach any target, as long as the underlying network remains connected. Feigenbaum et al. (ACM PODC 2012) and also Chiesa et al. (IEEE/ACM Trans. Netw. 2017) showed that it is not always possible to provide perfect resilience. Interestingly, not much more is known currently about the feasibility of perfect resilience. This paper revisits perfect resilience with local fast failover, both in a model where the source can and cannot be used for forwarding decisions. We first derive several fairly general impossibility results: By establishing a connection between graph minors and resilience, we prove that it is impossible to achieve perfect resilience on any non-planar graph; furthermore, while planarity is necessary, it is also not sufficient for perfect resilience. On the positive side, we show that graph families closed under link subdivision allow for simple and efficient failover algorithms which simply skip failed links. We demonstrate this technique by deriving perfect resilience for outerplanar graphs and related scenarios, as well as for scenarios where the source and target are topologically close after failures., Comment: To appear in the proceedings of the 2nd Symposium on Algorithmic Principles of Computer Systems (APOCS) 2021

Published
2021

11. Brief Announcement: What Can(Not) Be Perfectly Rerouted Locally

Author

Foerster, Klaus-Tycho, Hirvonen, Juho, Pignolet, Yvonne-Anne, Schmid, Stefan, Tredan, Gilles, University of Vienna [Vienna], Aalto University, DFINITY Foundation [Zurich], Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique (LAAS-TSF), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-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)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Attiya, Hagit, University of Vienna, Professorship Suomela J., DFINITY, Centre National de la Recherche Scientifique (CNRS), Department of Computer Science, and Aalto-yliopisto

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 2012 ACM Subject Classification Networks → Routing protocols, Resilience, Theory of computation → Distributed algorithms phrases Resilience, Computer systems organization → Dependable and fault-tolerant systems and networks, Theory of computation → Distributed algorithms, Networks → Routing protocols, Local Failover
Abstract

In order to provide a high resilience and to react quickly to link failures, modern computer networks support fully decentralized flow rerouting, also known as local fast failover. In a nutshell, the task of a local fast failover algorithm is to pre-define fast failover rules for each node using locally available information only. Ideally, such a local fast failover algorithm provides a perfect resilience deterministically: a packet emitted from any source can reach any target, as long as the underlying network remains connected. Feigenbaum et al. showed [Feigenbaum and others, 2012] that it is not always possible to provide perfect resilience; on the positive side, the authors also presented an efficient algorithm which achieves at least 1-resilience, tolerating a single failure in any network. Interestingly, not much more is known currently about the feasibility of perfect resilience. This brief announcement revisits perfect resilience with local fast failover, both in a model where the source can and cannot be used for forwarding decisions. By establishing a connection between graph minors and resilience, we prove that it is impossible to achieve perfect resilience on any non-planar graph; On the positive side, we can derive perfect resilience for outerplanar and some planar graphs., LIPIcs, Vol. 179, 34th International Symposium on Distributed Computing (DISC 2020), pages 46:1-46:3

Published
2020

12. TamperNN: Efficient Tampering Detection of Deployed Neural Nets

Author

Tredan Gilles, Erwan Le Merrer, the World Is Distributed Exploring the tension between scale and coordination (WIDE), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-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), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Fédérale Toulouse Midi-Pyrénées

Subjects
FOS: Computer and information sciences, 021110 strategic, defence & security studies, Class (computer programming), Computer Science - Machine Learning, Computer Science - Cryptography and Security, Artificial neural network, Computer science, Reliability (computer networking), Computer Vision and Pattern Recognition (cs.CV), Real-time computing, 0211 other engineering and technologies, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine Learning (cs.LG), Range (mathematics), [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, State (computer science), Quantization (image processing), Digital watermarking, Cryptography and Security (cs.CR), MNIST database, ComputingMilieux_MISCELLANEOUS
Abstract

Neural networks are powering the deployment of embedded devices and Internet of Things. Applications range from personal assistants to critical ones such as self-driving cars. It has been shown recently that models obtained from neural nets can be trojaned ; an attacker can then trigger an arbitrary model behavior facing crafted inputs. This has a critical impact on the security and reliability of those deployed devices. We introduce novel algorithms to detect the tampering with deployed models, classifiers in particular. In the remote interaction setup we consider, the proposed strategy is to identify markers of the model input space that are likely to change class if the model is attacked, allowing a user to detect a possible tampering. This setup makes our proposal compatible with a wide range of scenarios, such as embedded models, or models exposed through prediction APIs. We experiment those tampering detection algorithms on the canonical MNIST dataset, over three different types of neural nets, and facing five different attacks (trojaning, quantization, fine-tuning, compression and watermarking). We then validate over five large models (VGG16, VGG19, ResNet, MobileNet, DenseNet) with a state of the art dataset (VGGFace2), and report results demonstrating the possibility of an efficient detection of model tampering., Comment: In the 30th International Symposium on Software Reliability Engineering (ISSRE 2019)

Published
2019

13. On the Implications of Routing Models on Network Optimization.

Author

Pignolet, Yvonne-Anne, Schmid, Stefan, and Tredan, Gilles

Abstract

In network optimization problems, from traffic engineering to network monitoring, the routing model is typically considered as something given and frozen. This paper is motivated by the fundamental question how the ability to change and optimize the routing model itself influences the efficiency at which communication networks can be operated. To this end, we identify two main dimensions of a routing model: consistency (of a single route) and coherence (of sets of routes). We present analytical results on the impact of the routing model on the achievable route diversity as well as on the runtime of solving optimization problems underlying different case studies. We also uncover that it can sometimes be beneficial to artificially restrict the routing model, to significantly reduce the computational complexity without negatively affecting the route diversity much. [ABSTRACT FROM AUTHOR]

Published
2021
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14. Load-Optimal Local Fast Rerouting for Dense Networks.

Author

Borokhovich, Michael, Pignolet, Yvonne-Anne, Schmid, Stefan, and Tredan, Gilles

Subjects
COMPUTER networks, ROUTING (Computer network management)
Abstract

Reliable and highly available computer networks must implement resilient fast rerouting mechanisms: upon a link or node failure, an alternative route is determined quickly, without involving the network control plane. Designing such fast failover mechanisms capable of dealing with multiple concurrent failures, however, is challenging, as failover rules need to be installed proactively, i.e., ahead of time, without knowledge of the actual failures happening at runtime. Indeed, only little is known today about the design of resilient routing algorithms. This paper introduces a general framework to reason about and design local failover algorithms that minimize the resulting load after failover on dense networks, beyond destination-based routing. We show that due to the inherent locality of the failover decisions at runtime, the problem is fundamentally related to the field of distributed algorithms without coordination. We derive an intriguing lower bound on the inherent network load overhead any local fast failover scheme that will introduce in the worst case, even though globally seen, much more balanced traffic allocations exist. We then present different randomized and deterministic failover algorithms and analyze their overhead load. In particular, we build upon the theory of combinatorial designs and develop a novel deterministic failover mechanism based on symmetric block design theory, which tolerates a maximal number of link failures while ensuring low loads. [ABSTRACT FROM AUTHOR]

Published
2018
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15. Request Complexity of VNet Topology Extraction: Dictionary-Based Attacks

Author

Pignolet, Yvonne-Anne, Schmid, Stefan, and Tredan, Gilles

Subjects
Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Computer Science - Networking and Internet Architecture, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)
Abstract

The network virtualization paradigm envisions an Internet where arbitrary virtual networks (VNets) can be specified and embedded over a shared substrate (e.g., the physical infrastructure). As VNets can be requested at short notice and for a desired time period only, the paradigm enables a flexible service deployment and an efficient resource utilization. This paper investigates the security implications of such an architecture. We consider a simple model where an attacker seeks to extract secret information about the substrate topology, by issuing repeated VNet embedding requests. We present a general framework that exploits basic properties of the VNet embedding relation to infer the entire topology. Our framework is based on a graph motif dictionary applicable for various graph classes. Moreover, we provide upper bounds on the request complexity, the number of requests needed by the attacker to succeed., full version of paper at NETYS 2013

Published
2013

16. Centralité du second ordre : Calcul distribué de l'importance de noeuds dans un réseau complexe

Author

Kermarrec, Anne-Marie, Le Merrer, Erwan, Sericola, Bruno, Tredan, Gilles, As Scalable As Possible: foundations of large scale dynamic distributed systems (ASAP), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Dependability Interoperability and perfOrmance aNalYsiS Of networkS (DIONYSOS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), INRIA, SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]
Abstract

A complex network can be modeled as a graph representing the "who knows who" relationship. In the context of graph theory for social networks, the notion of centrality is used to assess the relative importance of nodes in a given network topology. For example, in a network composed of large dense clusters connected through only a few links, the nodes involved in those links are particularly critical as far as the network survivability is concerned. This may also impact any application running on top of it. Such information can be exploited for various topological maintenance issues to prevent congestion and disruptance. This can also be used offline to identify the most important nodes in large social interaction graphs. Several forms of centrality have been proposed so far. Yet, they suffer from imperfections : designed for abstract graphs, they are either of limited use (degree centrality), either uncomputable in a distributed setting (random walk betweenness centrality). In this paper we introduce a novel form of centrality : the second order centrality which can be computed in a fully decentralized manner. This provides locally each node with its relative criticity and relies on a random walk visiting the network in an unbiased fashion. To this end, each node records the time elapsed between visits of that random walk (called return time in the sequel) and computes the standard deviation (or second order moment) of such return times. Both through theoretical analysis and simulation, we show that the standard deviation can be used to accurately identify critical nodes as well as to globally characterize graphs topology in a fully decentralized way.

Published
2009

19. A GENERIC TRUST FRAMEWORK FOR LARGE-SCALE OPEN SYSTEMS USING MACHINE LEARNING.

Author

Liu, Xin, Tredan, Gilles, and Datta, Anwitaman

Subjects
*LARGE scale systems, *MACHINE learning, *WEB services, *RELIABILITY (Personality trait), *COMPUTER systems
Abstract

In many large-scale distributed systems and on the Web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable interaction environment. A traditional approach to reason about the risk of a transaction is to determine if the involved agent is trustworthy on the basis of its behavior history. As a departure from such traditional trust models, we propose a generic, trust framework based on machine learning where an agent uses its own previous transactions (with other agents) to build a personal knowledge base. This is used to assess the trustworthiness of a transaction on the basis of the associated features, particularly using the features that help discern successful transactions from unsuccessful ones. These features are handled by applying appropriate machine learning algorithms to extract the relationships between the potential transaction and the previous ones. Experiments based on real data sets show that our approach is more accurate than other trust mechanisms, especially when the information about past behavior of the specific agent is rare, incomplete, or inaccurate. [ABSTRACT FROM AUTHOR]

Published
2014
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21. Large-Scale Networked Systems: From Anarchy to Geometric Self-structuring.

Author

Kermarrec, Anne-Marie, Mostefaoui, Achour, Raynal, Michel, Tredan, Gilles, and Viana, Aline C.

Abstract

We define geometric self-structuring in a large-scale networked system as the ability of the participating nodes to collaboratively impose a geometric structure to the network. Self-structuring is hard to achieve when no global positioning information about the network is available. Yet this is an useful capability in networked autonomous systems such as sensor networks. In this paper, we present the design and the evaluation of a fully decentralized geometric self-structuring approach. This approach heavily relies on the ability of each node to estimate its position in the network. The contribution of the paper is twofold: (i) a simple and fully decentralized virtual coordinated system (VINCOS) is proposed, relying only on local connectivity information and per-neighbor communication; (ii) a network geometric self-structuring approach (NetGeoS) is presented that enables a large set of nodes to configure themselves in arbitrary geometric structures. The evaluation shows that the approach is both efficient and accurate while achieving the geometric structuring. [ABSTRACT FROM AUTHOR]

Published
2009
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23. Grafting Arborescences for Extra Resilience of Fast Rerouting Schemes

Author

Stefan Schmid, Andrzej Kamisinski, Gilles Tredan, Klaus-Tycho Foerster, Yvonne-Anne Pignolet, TREDAN, Gilles, University of Vienna [Vienna], Dfinity Switzerland, Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique (LAAS-TSF), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-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)-Université Toulouse Capitole (UT Capitole), and Université de Toulouse (UT)

Subjects
Spanning tree, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Computer science, Distributed computing, Grafting (decision trees), [INFO] Computer Science [cs], Network topology, Telecommunications network, Failover, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Forwarding plane, [INFO]Computer Science [cs], Resilience (network), Heterogeneous network
Abstract

International audience; To provide a high availability and to be able to quickly react to link failures, most communication networks feature fast rerouting (FRR) mechanisms in the data plane. However, configuring these mechanisms to provide a high resilience against multiple failures is algorithmically challenging, as rerouting rules can only depend on local failure information and need to be predefined. This paper is motivated by the observation that the common approach to design fast rerouting algorithms, based on spanning trees and covering arborescences, comes at a cost of reduced resilience as it does not fully exploit the available links in heterogeneous topologies. We present several novel fast rerouting algorithms which are not limited by spanning trees, but rather extend and combine ("graft") multiple spanning arborescences to improve resilience. We compare our algorithms analytically and empirically, and show that they can significantly improve not only the resilience, but also accelerate the preprocessing to generate the local fast failover rules.

Published
2021

24. Implications of Routing Coherence and Consistency on Network Optimization

Author

Pignolet, Yvonne-Anne, Schmid, Stefan, Trédan, Gilles, Dfinity Switzerland, University of Vienna [Vienna], Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique (LAAS-TSF), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-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)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and TREDAN, Gilles

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
Abstract

International audience; In network optimization problems, from traffic engineering to network monitoring, the routing model is typically considered as something given and fixed. This paper is motivated by the fundamental question how the ability to change and optimize the routing model itself influences the efficiency at which communication networks can be operated. To this end, we identify two main dimensions of the routing model: consistency (of a single route) and coherence (of sets of routes). We present analytical results on the impact of the routing model on the achievable route diversity as well as on the runtime of solving optimization problems underlying different case studies. We also uncover that it can sometimes be beneficial to artificially restrict the routing model, to significantly reduce the computational complexity without negatively affecting the route diversity much.

Published
2020

25. Adaptive Data Collection Mechanisms for Smart Monitoring of Distribution Grids

Author

Kemal, Mohammed Seifu, Olsen, Rasmus Løvenstein, Peter Schwefel, Hans, and Tredan, Gilles

Subjects
FOS: Computer and information sciences, Computer Science - Distributed, Parallel, and Cluster Computing, FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Systems and Control, Systems and Control (eess.SY), Distributed, Parallel, and Cluster Computing (cs.DC)
Abstract

Smart Grid systems not only transport electric energy but also information which will be active part of the electricity supply system. This has led to the introduction of intelligent components on all layers of the electrical grid in power generation, transmission, distribution and consumption units. For electric distribution systems, Information from Smart Meters can be utilized to monitor and control the state of the grid. Hence, it is indeed inherent that data from Smart Meters should be collected in a resilient, reliable, secure and timely manner fulfilling all the communication requirements and standards. This paper presents a proposal for smart data collection mechanisms to monitor electrical grids with adaptive smart metering infrastructures. A general overview of a platform is given for testing, evaluating and implementing mechanisms to adapt Smart Meter data aggregation. Three main aspects of adaptiveness of the system are studied, adaptiveness to smart metering application needs, adaptiveness to changing communication network dynamics and adaptiveness to security attacks. Execution of tests will be conducted in real field experimental set-up and in an advanced hardware in the loop test-bed with power and communication co-simulation for validation purposes., 5 pages, 2 figures , Hans-Peter Schwefel. 12th European Dependable Computing Conference (EDCC 2016), September 5-9, 2016, Gothenburg, Sweden. Proceedings of Student Forum - EDCC 2016

Published
2016

26. Collective information processing in human phase separation.

Author

Jayles B, Escobedo R, Pasqua R, Zanon C, Blanchet A, Roy M, Tredan G, Theraulaz G, and Sire C

Subjects
Humans, Models, Psychological, Group Processes, Interpersonal Relations, Pedestrians psychology
Abstract

In our digital societies, individuals massively interact through digital interfaces whose impact on collective dynamics can be important. In particular, the combination of social media filters and recommender systems can lead to the emergence of polarized and fragmented groups. In some social contexts, such segregation processes of human groups have been shown to share similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregation behaviour of human groups. We report a series of experiments where groups of 22 subjects have to perform a collective segregation task that mimics the tendency of individuals to bond with other similar individuals. More precisely, the participants are each assigned a colour (red or blue) unknown to them, and have to regroup with other subjects sharing the same colour. To assist them, they are equipped with an artificial sensory device capable of detecting the majority colour in their 'environment' (defined as their k nearest neighbours, unbeknownst to them), for which we control the perception range, k = 1, 3, 5, 7, 9, 11, 13. We study the separation dynamics (emergence of unicolour groups) and the properties of the final state, and show that the value of k controls the quality of the segregation, although the subjects are totally unaware of the precise definition of the 'environment'. We also find that there is a perception range k = 7 above which the ability of the group to segregate does not improve. We introduce a model that precisely describes the random motion of a group of pedestrians in a confined space, and which faithfully reproduces and allows interpretation of the results of the segregation experiments. Finally, we discuss the strong and precise analogy between our experiment and the phase separation of two immiscible materials at very low temperature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Published
2020
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