Search

The evaluation and accreditation of competencies is one of the challenges that higher education must address in the 21st century. GRAF is a system of evaluation and accreditation of curricular competencies based on the integration of pedagogical engineering methodologies with data architecture design and technological applications, especially tailored to online higher education. From the teaching point of view, it provides a model, a methodology, and a tool for designing competency-based programmes and assessing the evidence of competency learning generated by the students in the teaching and learning process throughout the semester. From the student’s perspective, it enhances conventional evaluation by accompanying marks with educational feedback on the level of competency achieved, and provides a graphic representation of progress. Specific tools and applications have been developed for each phase of work: for the design stage, the Competency Design Tool; for the evaluation and monitoring stage, the Competency Assessment Tool; and for the graphic representation stage, the Competency Graph Report. The Universitat Oberta de Catalunya (UOC) has spent two years developing GRAF. It has been applied to five programmes in different areas: an MBA, two master’s degrees, a postgraduate course, and a university extension course. A total of 56 compulsory subjects and 293 students have been involved in these programmes. Due to the success of the initiative, the UOC has decided on a widespread application of GRAF to other programmes. We are interested in sharing the experience and lessons learned in order to improve GRAF.

The understanding of human ageing contributes to the overall improvement of healthcare and opens the door to the increase of life expectancy and quality of life. Studies on human ageing, which are evidence-based, require data. Although some efforts have been put to concentrate datasets in single reference points, many datasets remain hidden in articles, studies and research projects websites. In this paper, we aim at identifying those datasets that have been used for the study of human ageing and make them easy to find to researchers in the field. To do so, we have analysed wellknown literature databases, previous reviews, and specialised sources. We have reviewed the available data and, as a result, we have identified and discussed 28 datasets. Hence, this article provides an organised reference point to datasets for researchers interested in the study of human ageing and contributes to their visibility.

Predicting people’s next location has attracted the attention of both scientists and large Internet companies, for a variety of reasons. The analysis of location data collected from smart mobile devices paves the way for the improvement of current location based services and the rise of new business models, based on rich notifications related to the right prediction of users’ next location. Moreover, the so-called attention technique has been adopted in neural networks learning, aiming at considering alignments between different parts of the source training data. This article proposes the model Move, Attend and Predict (MAP) to predict a person’s future location based on his/her mobility pattern collected by a mobile device. This is achieved by means of a computationally efficient trainable deep neural network model. Our model essentially learns which time interval in the trajectory sequences are relevant regarding a specific location. In order to extract the meaningful representation from trajectories and time sequences, the embedding representation learning technique is used. Experimental results, obtained from tests conducted on two large real-life datasets, demonstrate that our model outperforms state-of-the-art models in terms of precision, recall and F1-score performance metrics.

Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location prediction accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to discover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of classical MC. Our proposed model considers both space and time contexts where the space represents a specific location that has been visited by a user while the time represents the location visiting time. We show how classical MC model can be extended to include movement times and how time will improve prediction accuracy. One unique finding in our research is related to the value of integrating users’ mobility location/space with time context. In particular, time context is formulated in a way to add extra information to the space context. For better abstraction during building the model, a general transformation function is used to transform the n -order MC into first order. Results show that the proposed approach provides significant improvements in predicting people’s next location compared with the state-of-the-art models when applied on two large real-life datasets, GeoLife and Gowalla.

The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

Smart cities have sensors and communication infrastructures that provide humongous amounts of data. People can extend this infrastructure, acting as independent, mobile sensors through their smartphones, enabling opportunistic sensing. This information provides locality and timeliness of measurements that would otherwise be unavailable. Researchers have developed a context-aware healthy-route recommender system that offers personalized recommendations to people according to their medical condition and real-time information from the smart city. Experiments with a simulated dataset and real data verified the system’s usefulness.

Worldwide, thousands of video surveillance cameras record our daily activities. People are aware that video surveillance is deployed for the sake of security. However, the privacy of individuals would be endangered if the proper measures were not considered. Privacy-aware video surveillance has historically been addressed by proposals based on detecting individuals and other sensitive parts of the video and hiding them using a variety of techniques. In this paper, we present a comprehensive solution tackling video processing, video protection and management of the Information System. We claim that a video surveillance system can protect our safety and, at the same time, guarantee our privacy. We describe the design and implementation of a privacy-aware video surveillance platform that, in order to be trustworthy, accomplishes with the properties of high detection accuracy, real-time performance and protected video utility. We have tested the proposed platform, and we demonstrate the feasibility of our approach for privacy protection.

Process Mining comprises a consolidated set of techniques that aim at discovering, checking and improving processes. Originally designed to cope with business processes, process mining techniques fed on log files automatically generated by enterprise resource planning (ERP) systems and software alike. However, nowadays, with the unstoppable deployment of the Internet of Things, multiple events are continuously generated and stored by distributed, highly uncoupled and heterogeneous devices able to sense their surroundings and report precise and timely updates on the environment. By embracing the Internet of Events, recently available contextual information opens the door to the application of process mining techniques in new domains, hence, generating opportunities for the analysis of contextual data with multiple implications in hot research areas such as, smart environments (e.g., smart cities, smart buildings, smart homes), cognitive systems, and industry 4.0. In this article, we discuss the opportunities and challenges of process mining in these new, events-rich, context-aware environments.

Early Mobilisation routines aim at improving the recovery process of critically ill patients that are hospitalised in intensive care units. Such routines consist of passive and active progressive mobilisations aiming at reducing the side-effects derived from ICU stays. In this paper, we present e-PEMICU, an e-health platform to support early mobilisation programmes in intensive care units. Our solution is founded on motion sensors and smartphones, hence, it is affordable and easy to deploy in real scenarios. We present the design and implementation of a prototype that has been created to address real users needs. Our platform paves the way for a better and sustainable application of current early mobilisation routines.

The healthcare sector is rapidly changing to incorporate new technologies Antoni that enable the collection of unprecedented amounts of data. New paradigms such as mobile and smart healthcare are founded on these new technologies, such as the IoT, and the data they collect are no longer only medical but, instead, they refer to contextual information with spatio-temporal features. Also, new data processing techniques, such as process mining, are gaining attention and are reshaping the landscape of healthcare information systems. In this article, we concentrate on Geographic Information Systems and propose HGIS: a comprehensive, healthcare-oriented, GIS architecture to support the integration of heterogeneous data with spatio-temporal features. Moreover, we comment on initial results obtained from a proof-of-concept implementation developed by Xarxa Sanitaria i Social de Santa Tecla, a large healthcare provider in the south of Catalonia.

Face de-identification aims at preserving the privacy of people by concealing faces in images and videos. In this paper, we propose a defeating algorithm for face de-identification methods that are based on DCT-block scrambling. These methods protect faces by scrambling the AC and DC coefficients of the DCT blocks corresponding to a face region in the compressed domain. The proposed approach does not make use of the protection key utilized in the de-identification process. It consists of the following stages. First, random unprotected faces are generated based on a random alteration of the sign of AC coefficients with a fixed value of DC coefficients. Then, the best unprotected faces are selected by an eigenfaces model trained with facial images from a repository of potentially protected people. A single facial image is then generated by merging the selected images through median stacking. Finally, the eigenfaces model is utilized again to choose the face from the repository that is closest to the resulting image in order to improve the aspect of the unprotected face. Experimental results using a proprietary database and the public CALTECH, Utrecht and LFW face databases show the effectiveness of the proposed technique.

The existence of certain comorbidities, the co-occurrence of different diseases in the same individual, is well-known in the medical community. However, finding temporal cause-effect relations between those diseases constitutes a big challenge. Clinical records can be used to extract the required information, but their analysis is elusive due to the vast and heterogeneous amount of data. We propose a new methodology for time-preserving tensor networks decomposition to be applied in the analysis of big data problems where the temporal dimension of the key factual fields must not be modified. This methodology will also allow the creation of a new process mining modeling which can capture the cause-effect relations as low-order tensors associated to the transitions of the mined processes, and whose structure takes the form of a multilayer complex network. All these theoretical and methodological advances will allow their application to real biomedical data to analyze comorbidities.

Smartphones are used worldwide for a variety of purposes: constant communication, immediate access to a humongous amount of knowledge, effortless ways of posting pictures, feelings and thoughts to name a few. In consequence, smartphones have become our natural life companions and, thanks to the rise of concepts such as mobile health and smart health, will be a linchpin regarding our care. Detecting the appearance of wandering behaviours in the elderly population is crucial in order to tackle the early treatment of mild cognitive impairments. To that end, a number of proposals have appeared, either theoretical methods or practical applications. This paper addresses the assessment of wandering detection methods from different perspectives. First, we review the available datasets in the literature that can be used for benchmarking wandering detection methods. Moreover, we analyse the available datasets using a well-known wandering detection technique, so as to obtain the number of abnormal trajectories with respect to the whole dataset and each individual. Finally, we discuss the properties that a dataset for benchmarking should fulfil.

The detection of cry is crucial in intelligent computerized systems that aim at assessing the well-being of neonates during their hospitalization periods. Moreover, a precise characterization of cry allows its classification (e.g., hunger, pain, tiredness…). Although several cry detection and characterization techniques can be found in the literature, there is no testing of such techniques in real-life environments such as hospital intensive care units. In this article, we first summarize the problem of background noise in intensive care units that may prevent the operation of cry detection algorithms from succeeding. Second, we implement a specific cry detection technique that is based on some of the relevant cry detection proposals that have been found in the literature. Finally, we test this method using audio samples recorded in a real neonatal intensive care unit.

Recent advances in pervasive video surveillance systems pave the way for a comprehensive surveillance of every aspect of our lives, hence, leading us to a state of dataveillance. Computerized and interconnected systems of cameras could be used to profile, track and monitor individuals for the sake of security. Notwithstanding, these systems clearly interfere with the fundamental right of the individuals to privacy. Most literature on privacy in video surveillance systems concentrates on the goal of detecting faces and other regions of interest and in proposing different methods to protect them. However, the trustworthiness of those systems and, by extension, of the privacy they provide are mostly neglected. In this article, we define the concept of trustworthy privacy-aware video surveillance system. Moreover, we assess the techniques proposed in the literature according to their suitability for such a video surveillance system. Finally, we describe the properties that a deployment of a trustworthy video surveillance system must fulfill.

10.1109/MCOM.2014.6871673 The new era of mobile health ushered in by the wide adoption of ubiquitous computing and mobile communications has brought opportunities for governments and companies to rethink their concept of healthcare. Simultaneously, the worldwide urbanization process represents a formidable challenge and attracts attention toward cities that are expected to gather higher populations and provide citizens with services in an efficient and human manner. These two trends have led to the appearance of mobile health and smart cities. In this article we introduce the new concept of smart health, which is the context-aware complement of mobile health within smart cities. We provide an overview of the main fields of knowledge that are involved in the process of building this new concept. Additionally, we discuss the main challenges and opportunities that s-Health would imply and provide a common ground for further research.

In this paper, we present the concept of double-phase microaggregation as an improvement of classical microaggregation for the protection of privacy in distributed scenarios without fully trusted parties. We apply this new concept in the context of mobile health and we show that a distributed architecture consisting of patients and several intermediate entities can apply it to protect the privacy of patients, whose data are released to third parties for secondary use. After recalling some fundamental concepts of statistical disclosure control and microaggregation, we detail the distributed architecture that allows the private gathering, storage, and sharing of biomedical data. We show that double-phase multivariate microaggregation properly fits the needs for privacy preservation of biomedical data in the distributed context of mobile health. Moreover, we show that double-phase microaggregation performs similarly to classical microaggregation in terms of information loss, disclosure risk, and correlation preservation, while avoiding the limitations of a centralized approach.

Checking data possession in networked information systems such as those related to critical infrastructures (power facilities, airports, data vaults, defense systems, etc.) is a matter of crucial importance. Remote data possession checking protocols permit to check that a remote server can access an uncorrupted file in such a way that the verifier does not need to know beforehand the entire file that is being verified. Unfortunately, current protocols only allow a limited number of successive verifications or are impractical from the computational point of view. In this paper, we present a new remote data possession checking protocol such that: 1) it allows an unlimited number of file integrity verifications; 2) its maximum running time can be chosen at set-up time and traded off against storage at the verifier.

Location-based services (LBS) will be a keystone of the new information society that is founded on the information and communications technologies (ICTs). Mobile devices such as cell phones or laptops have become ubiquitous. They are equipped with a variety of localisation systems that make them proper for making use of the new LBS. Most of the times, these services are provided by a trusted company (e.g. a telecommunications company). However, the massive use of mobile devices pave the way for the creation of ad hoc wireless networks that can be used to exchange information based on locations. When the exchange of location information is done amongst untrusted parties, the privacy of the participants could be in jeopardy. In this paper we present a novel solution that guarantees the privacy of the users of LBS. Our technique is built up of several modules that progressively increase the privacy level of the users. Unlike the existing approaches, our proposal does not rely on a trusted third party (TTP) to anonymise the users and to guarantee their location privacy.

The fast growth of Radio Frequency IDentification (RFID) implies a deployment challenge, namely how to keep this technology scalable without renouncing security and privacy features. This paper focuses on combining tag privacy and scalability using the hash locks mechanism. Our contribution is twofold: (i) a cell-based architecture is proposed in which the readers co-operate in order to conduct tag identification in a private and scalable way; (ii) a communication protocol for the proposed architecture is defined and assessed. The proposed architecture and protocol solve the scalability shortcomings of private RFID identification pointed out by Juels and Weis.

The SIMPATIC project (acronym for Intelligent System for the Private and Autonomous Surveillance based on Information and Communication Technologies, in Catalan) addresses the building of a monitoring system for people affected with Mild Cognitive Impairments (MCI). It consists of a mobile app running on the patient's smartphone and a server that analyses the device's location and raises alarms under certain conditions. This is a research project that comprises both technological and human challenges. Regarding the technological aspects, we have not merely built a testbed prototype: our technology has been tested in a real-life scenario. In this paper we address the implementation aspects and technological results of the pilot test conducted within the SIMPATIC project. We describe the architecture of the SIMPATIC system as well as its functionalities, but we focus on the implementation aspects that make our software a fully functional monitoring system based on smartphones and web technology.

Microaggregation is a family of methods for statistical disclosure control (SDC) of microdata (records on individuals and/or companies), that is, for masking microdata so that they can be released while preserving the privacy of the underlying individuals. The principle of microaggregation is to aggregate original database records into small groups prior to publication. Each group should contain at least k records to prevent disclosure of individual information, where k is a constant value preset by the data protector. Recently, microaggregation has been shown to be useful to achieve k-anonymity, in addition to it being a good masking method. Optimal microaggregation (with minimum within-groups variability loss) can be computed in polynomial time for univariate data. Unfortunately, for multivariate data it is an NP-hard problem. Several heuristic approaches to microaggregation have been proposed in the literature. Heuristics yielding groups with fixed size k tends to be more efficient, whereas data-oriented heuristics yielding variable group size tends to result in lower information loss. This paper presents new data-oriented heuristics which improve on the trade-off between computational complexity and information loss and are thus usable for large datasets.

Thousands of video files are stored in surveillance databases. Pictures of individuals are considered personal data and, thus, their disclosure must be prevented. Although video surveillance is done for the sake of security, the privacy of individuals could be endangered if the proper measures are not taken. In this chapter we claim that a video-surveillance system could protect our safety and, at the same time, guarantee our privacy. Most literature on privacy in video surveillance systems concentrates on the goal of detecting faces and other regions of interest, and in proposing different methods to protect them. However, the trustworthiness of those systems and, by extension, of the privacy they provide are neglected. Hence, we define the concept of Trustworthy Video Surveillance System (T-VSS), which tackles the issue of protecting the privacy of the individuals. In this chapter, we assess the techniques proposed in the literature according to their suitability in a T-VSS. Moreover, we describe a privacy-aware video-surveillance platform that fulfils those properties and we detail all its components. We have implemented and tested the proposed platform to show the feasibility of our proposal.

This paper defines a new methodology for trustworthy video surveillance databases, which fulfills four crucial properties: high accuracy, reversibility, real-time performance and information security. We detect the regions of interest (e.g. faces of people), which are protected by means of the alteration of the coefficients of the compressed video stream. The proposed protection guarantees the property of reversibility, since the original coefficients can be restored. Specifically, we elaborate on the definition of the Protection Stream, which is required for both protecting and unprotecting a video sequence. We propose a procedure for securely generating a protection stream for each group of pictures, thus avoiding the large unsecure streams required if the protection stream was generated for each video file. The proposed model has been implemented and tested, and the results confirm the real time performance of the system developed while keeping the aforementioned crucial properties.

10.1109/JSAC.2013.SUP.0513002 Age-related diseases are becoming more prominent due to life expectancy increase in developed countries. Mild cognitive impairment and several types of dementia like Alzheimer's disease are gaining importance both socially and economically. Patients suffering from these diseases have different degrees of autonomy and, thus, different needs. Often, relatives or friends take care of those patients. However, during the first stages of the disease, they still have a high degree of autonomy and frown on the supervision of others. Despite their autonomy, patients could get lost and disoriented. Rapidly determining the location of a lost patient is paramount to reduce the risk of suffering serious injuries. Current solutions to this problem are based on the continuous monitoring of the patient. Such continuous control might be seen by most people as a privacy invasion, and it may discourage patients from using these solutions. In this article we present the concept of m-Carer as a smart mobile device able to privately monitor the movements of patients having diverse degrees of mobility and autonomy. After justifying the need for privacy-aware m-carers due to social and economical reasons, we propose a complete architecture aimed at fulfilling the needs of patients, relatives and healthcare services. Moreover, we show a real implementation of our proposal so as to confirm that it is technically sound and feasible.

10.1109/MCOM.2013.6525606 Cities are growing steadily, and the process of urbanization is a common trend in the world. Although cities are getting bigger, they are not necessarily getting better. With the aim to provide citizens with a better place to live, a new concept of a city was born: the smart city. The real meaning of smart city is not strictly defined, but it has gained much attention, and many cities are taking action in order to be considered 'smart'. These smart cities, founded on the use of information and communication technologies, aim at tackling many local problems, from local economy and transportation to quality of life and e-governance. Although technology helps to solve many of these local problems, their ability to gather unprecedented amounts of information could endanger the privacy of citizens. In this article we identify a number of privacy breaches that can appear within the context of smart cities and their services. We leverage some concepts of previously defined privacy models and define the concept of citizens' privacy as a model with five dimensions: identity privacy, query privacy, location privacy, footprint privacy and owner privacy. By means of several examples of smart city services, we define each privacy dimension and show how existing privacy enhancing technologies could be used to preserve citizens' privacy.

The consideration of security and privacy is a linchpin of the social acceptance of pervasive technology. This paper paves the way to the development of trustworthy pervasive video surveillance systems, by emphasizing the need to properly combine different aspects that current systems do not manage. In particular, in this paper we propose the combination of the following issues into a common framework: proper people identification mainly based on computer vision techniques, content protection not only by using convenient cryptographic techniques, but also law enforcement and user cooperation in order to get feedback with regard to the whole video surveillance system. Furthermore, an analysis focused on the current computer vision techniques used for people identification is presented. Finally, a score to measure the trust offered by video surveillance systems is proposed.

The book is a collection of invited papers on Computational Intelligence for Privacy and Security. The majority of the chapters are extended versions of works presented at the special session on Computational Intelligence for Privacy and Security of the International Joint Conference on Neural Networks (IJCNN-2010) held July 2010 in Barcelona, Spain. The book is devoted to Computational Intelligence for Privacy and Security. It provides an overview of the most recent advances on the Computational Intelligence techniques being developed for Privacy and Security. The book will be of interest to researchers in industry and academics and to post-graduate students interested in the latest advances and developments in the field of Computational Intelligence for Privacy and Security.

Protecting the privacy of individuals, whose data are released to untrusted parties, is a problem that has captured the attention of the scientific community for years. Several techniques have been proposed to cope with this problem. Amongst these techniques, microaggregation is able to provide a good trade-off between information loss and disclosure risk. Thus, many efforts have been devoted to its study. Microaggregation is a statistical disclosure control (SDC) technique that aims at protecting the privacy of individual respondents by aggregating the information of similar respondents, so as to make them undistinguishable. Although microaggregation is a very interesting approach, to microaggregate multivariate data sets optimally is known to be an NP-hard problem. Consequently, the use of heuristics has been suggested as a possible strategy to solve the problem in a reasonable time. Specifically, genetic algorithms (GA) have been shown to be able to find good solutions to the microaggregation problem for small, multivariate data sets. However, due to the very nature of the problem, GA can hardly cope with large, multivariate data sets. With the aim to apply them to large data sets, those have to be previously partitioned into smaller disjoint subsets that the GA can handle separately. In this chapter, we summarise several proposals for partitioning data sets, in order to apply GA to microaggregate them. In addition, we elaborate on the study of a partitioning strategy based on the variable-MDAV algorithm, we study the effect of several parameters, namely the dimension, the aggregation parameter (k), the size of the data sets, etc. Also, we compare it with the most relevant previous proposals.

In daily life, people are faced with problems that have more than one solution. In computing, and other fields, researchers and developers encounter computational problems that may have a large number of solutions. In these cases, it may be almost unfeasible to determine the best one. Evolutionary computation is an appropriate technique for finding an optimal solution to a problem. Among these problems, evolutionary computation can provide an efficient way to solve those associated with computer security and forensics. This chapter examines the approaches evolutionary computation offers to discover an optimal solution to a problem. Moreover, it overviews how evolutionary computation can be applied to different scenarios related to computer security and forensics.

Services in the information society allow automatically and routinely collecting large amounts of data. Those data are often used to train classification rules in view of making automated decisions, like loan granting/denial, insurance premium computation, etc. If the training datasets are biased in what regards sensitive attributes like gender, race, religion, etc., discriminatory decisions may ensue. Direct discrimination occurs when decisions are made based on biased sensitive attributes. Indirect discrimination occurs when decisions are made based on non-sensitive attributes which are strongly correlated with biased sensitive attributes. This paper discusses how to clean training datasets and outsourced datasets in such a way that legitimate classification rules can still be extracted but indirectly discriminating rules cannot.

Microaggregation is a Statistical Disclosure Control (SDC) technique that aims at protecting the privacy of individual respondents before their data are released.

Privacy and security are paramount in the proper deployment of location-based services (LBSs). We present a novel protocol based on user collaboration to privately retrieve location-based information from an LBS provider. Our approach does not assume that users or providers can be completely trusted with regard to privacy, and does not rely on a trusted third party. In addition, user queries, containing accurate locations, remain unchanged, and the collaborative protocol does not impose any special requirements on the query–response function of the LBS. Our protocol is analyzed in terms of privacy, network traffic, and processing overhead. We define the concept of guaranteed privacy breach probability, and we show that our proposal provides exponential scalability in that probability, at the expense of a linear relative network cost.

Mobile devices able to locate themselves such as mobile phones and PDAs are virtually everywhere, and they are expected to gain more importance in the near future. Their ability to determine locations opens the door to a new bunch of services: the so-called location-based services (LBS). The commercial sector will highly benefit from LBS. However, they are not without a cost — privacy. In this article we present a (trusted third party)-free, distributed, collaborative method to preserve the location privacy of LBS users.

Protecting personal data is essential to guarantee the rule of law1. Due to the new Information and Communication Technologies (ICTs) unprecedented amounts of personal data can be stored and analysed. Thus, if the proper measures are not taken, individual privacy could be in jeopardy. Being the aim to protect individual privacy, a great variety of statistical disclosure control (SDC) techniques has been proposed. Amongst many others, k-anonymity is a promising property that, if properly achieved, can help protect individual privacy. In this paper, we propose a new post-processing method that can be applied after a k-anonymity algorithm, being the aim to lessen the errors resulting from the aggregation of data. We show that our method can be extended to work with many other SDC techniques and we provide some experimental results which emphasise the usefulness of our proposal.

Information and communications technologies (ICT) are fostering the appearance of a plethora of new services and applications. On the one hand, ICT will improve the efficiency of a variety of processes (e.g. supply chains, e-commerce, etc.) and they will make easy the development of new services. On the other hand, the massive deployment of these new technologies may lead to the invasion of users privacy. In this article, we analyse four of the main technologies which can put the privacy of their users in jeopardy: Internet browsing, location-based services, RFID technology and statistical databases.

Location-Based Services (LBS) can be accessed from a variety of mobile devices to obtain value added information related to the location of the user. Most of the times, these services are provided by a trusted company (e.g. a telecommunications company). However, the massive use of mobile devices pave the way for the creation of ad hoc wireless networks that can be used to exchange information based on locations. In the latter case, these LBS could be provided by an untrusted party. Sending the location to an untrusted LBS provider could put the privacy of the user in jeopardy. In this paper we propose a novel technique to guarantee the privacy of users of LBS. Our technique consists of several modules, but the highest degree of security is achieved thanks to the use of a public-key privacy homomorphism. Unlike the existing approaches, our proposal does not need any trusted third party to anonymise the users and only makes use of a public-key infrastructure.

Microaggregation is a clustering problem with cardinality constraints that originated in the area of statistical disclosure control for micro data. This article presents a method for multivariate microaggregation based on genetic algorithms (GA). The adaptations required to characterize the multivariate microaggregation problem are explained and justified. Extensive experimentation has been carried out with the aim of finding the best values for the most relevant parameters of the modified GA: the population size and the crossover and mutation rates. The experimental results demonstrate that our method finds the optimal solution to the problem in almost all experiments when working with small data sets. Thus, for small data sets the proposed method performs better than known polynomial heuristics and can be combined with these for larger data sets. Moreover, a sensitivity analysis of parameter values is reported which shows the influence of the parameters and their best values

Intellectual property protection of multimedia is a thorny and largely unsolved problem. Despite its practical limitations, copy detection is an excellent complement to copy prevention. Fingerprinting techniques make redistributor tracing possible by relying on invisible watermarks embedded in the content sold which are unique for each particular buyer. That uniqueness of fingerprints is not easy to achieve in multicast transmission. We argue in this paper that encryption-based solutions are the most realistic option to fingerprint multicast transmission. However, the few existing encryption-based methods in the literature are vulnerable to buyer collusions. We propose an encryption-based algorithm which provides collusion security at the cost of transmitting the content twice.

New mobile communication technologies are expected to be broadly available in the near future. Those technologies (multimedia-enabled devices and broadband access to the Internet) will enable new services involving video transmission. Service providers are likely to want the customer to pay for contents, whereas they will also want their intellectual property protected. We present a system that integrates content, fine-grain pay-per-view and copyright protection with a rich variety of multimedia output devices and transmission networks.

As new mobile communication technologies are becoming broadly available, there is urgent pressure to populate them with services that provide returns for the huge investments made by telecommunications operators. Services around video transmission are expected to play a key role: news broadcasting, videoconferencing, movie channels, on-line gambling, etc. The goal of the STREAMOBILE project is to demonstrate an Internet pay-per-view distribution system toward GPRS and UMTS mobile devices. Copyright-protected contents are streamed to the customer and a micropayment scheme is used so that contents are not paid for in advance, but as they are being received by the customer. This paper presents an evolving prototype., whose client software has been designed in such a way that it can be easily migrated to 3G mobile devices.

Fingerprinting schemes allow tracing of illegally redistributed multimedia contents. When using asymmetric fingerprinting, the merchant does not know the marked copy nor the embedded mark, so fraudulent distribution of the content by the merchant - which could lead to framing honest customers - is not possible. A problem with the deployment of classical asymmetric fingerprinting protocols is that they are based on information transfer methods whose implementation is difficult, if not infeasible. We propose an architecture to obtain asymmetric fingerprinting through symmetric (and thus implementable) schemes and a trusted third party (TTP).

Multicast content delivery can be expected to become a major source of revenue with the increase of private broadband fixed and mobile communications. Several multicast applications require the receivers to securely send some real-time information back to the source, which leads to a many-to-one communication scenario. Using unicast connections to send this reverse traffic results in a data implosion which may swamp the source with incoming communication. In order to avoid this problem, a method for aggregating the information as it is being sent back to the source is presented in this paper. Confidentiality and authentication are guaranteed at the symbol level for this reverse realtime traffic.

When designing a secure Internet version for keno or bingo for a large group of players, a multicast communication model is likely to be used for source-to-players communication. For a large group of players, this can potentially overwhelm the source, a problem which is known as implosion. We present a scheme for large-scale bingo which avoids implosion in the many-to-one communication between the players and the hall. The resulting scheme aggregates information using a probabilistic public-key additive privacy homomorphism and guarantees the security of communications.

This paper addresses the problem of pay-as-you-watch services over unicast and multicast communications. For each communication model, we present two solutions, non-verifiable and verifiable, depending on the existence or non-existence of trust between the source and the receiver(s). In verifiable schemes, the source obtains a proof of correct reception by the receiver(s); in non-verifiable schemes, receiver non-repudiation is not guaranteed, so there must be a trust relationship between source and receiver(s). While solutions for unicast pay-as-you-watch can be based on existing technologies, novel algorithms based on aggregation and multisignatures are needed and presented here to overcome implosion in multicast pay-as-you-watch.

Generation of a synthetic microdata set that reproduces the statistical properties of an original microdata set is a promising approach to statistical disclosure control (SDC) of microdata. In this paper, a new method for generating continuous synthetic microdata is proposed. The covariance matrix and the univariate statistics of the original data set are exactly preserved. The method is non-iterative and its complexity grows linearly with the number of records to be protected.

The knowledge of computer applications such as word processing or Internet-based communication is essential in most jobs. On the other hand, most professional training programs for unemployed people contain a computer course for those who have never used a computer. Middle-aged adults present several difficulties in order to learn core computer skills, which must be overcome in order for these people to be able to handle basic computer applications successfully. In this paper, we present a 50 hour program to teach computer skills for middle-aged adults.

Successive failure of copy prevention systems has caused copy detection to become the most promising option to protect the intellectual property of multimedia content. Fingerprinting techniques rely on invisible watermarks embedded in the copy sold in order to identify the buyer. We address the problem of security against collusion attacks in proposals for fingerprinting in a multicast environment. A proposal that combines Boneh-Shaw collusion-secure codes and encryption-based fingerprinting is presented that results in a collusion-secure fingerprinting system for multicast communications.

The amount of computer-stored information is growing faster with each passing day. This growth and the way in which the stored data are accessed through a variety of channels have raised the alarm about the protection of the individual privacy of the respondents whose data are being collected and stored. On the one hand, data should be available to researchers and statistical agencies so that the necessary research and planning activities can be conducted. However, on the other hand, the right of respondents to privacy must be protected. Statistical disclosure control (SDC) is the discipline which cares about keeping a balance between data access and privacy protection. k-Anonymity is one particular approach to SDC for individual data (microdata): the record corresponding to a specific respondent is k-anonymous if an intruder can at best link the record to a group of k respondents containing the correct one. This paper surveys the use of a special clustering technique called microaggregation to provide k-anonymity.