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International audience ; —In this paper we present our system Faces of Politics (henceforth FoP), that is able to continuously learn multimedia knowledge of Web multimedia resources about the presence of person(s) in a pictures and to leverage this knowledge to the Linked Open Data cloud (LOD-cloud). FoP promotes both scalability of the data lift process for this domain and a structured knowledge representation for complex queries. The system was bootstraped using Freebase data about politicians and their pictures, and we show that the model provides a good generalization with an error rate below 7%. Meantime, FoP not only relates a person to a multimedia resource, but it also detects and publishes metadata on the position of the person in the picture. Moreover, it supports the presence of several persons in the picture. At this step, FoP is also giving data in return to the LoD cloud that fed him in the first place: it leverages Linked Data on people recognized in these pictures, and on which rectangle area. This allows fine-grained queries like creating a curation of documents in which a person is depicted relatively to another for instance. On a technical point-of-view, we also provide a Website for browsing FoP knowledge base as Web users, and we also offer a public SPARQL endpoint for robots or other Web applications.

International audience ; OpenData movement around the globe is demanding more access to information which lies locked in public or private servers. As recently reported by a McKinsey publication, this data has significant economic value, yet its release has potential to blatantly conflict with people privacy. Recent UK government inquires have shown concern from various parties about publication of anonymized databases, as there is concrete possibility of user identification by means of link- age attacks. Differential privacy stands out as a model that provides strong formal guarantees about the anonymity of the participants in a sanitized database. Only recent results demonstrated its applicability on real-life datasets, though. This paper covers such breakthrough discoveries, by review- ing applications of differential privacy for non-interactive publication of anonymized real-life datasets. Theory, util- ity and a data-aware comparison are discussed on a variety of principles and concrete applications.

International audience ; The European Union has created the European Institute of Technology (EIT), within which ICT Labs focuses on fostering exchange and new result creation in the sphere of Information and Communication Technology, across the areas of research, higher education, and industrial innovation. ''Digital Cities of the Future'' is an action line (or chapter) of EIT ICT Labs. Within this action line, we coordinated an activity called ''DataBridges: Data Integration for Digital cities'', whose aim is to produce, link, integrate and exploit open data in the Digital Cities data space, for the benefit of both citizens and administrations. In that context, DataBridges addresses many research challenges such as acquiring (or producing) Open City Data in RDF, data linking and integration, data provenance, and visualization. This position paper describes our efforts within DataBridges to integrate City Data. We provide a brief introduction to EIT ICT Labs, its goals and structures, and how DataBridges fits them. We then detail the activity and some selected results from 2011 - 2012, and plans for 2013. We conclude with some research challenges we plan to adress in the future.

International audience ; The European Union has created the European Institute of Technology (EIT), within which ICT Labs focuses on fostering exchange and new result creation in the sphere of Information and Communication Technology, across the areas of research, higher education, and industrial innovation. ''Digital Cities of the Future'' is an action line (or chapter) of EIT ICT Labs. Within this action line, we coordinated an activity called ''DataBridges: Data Integration for Digital cities'', whose aim is to produce, link, integrate and exploit open data in the Digital Cities data space, for the benefit of both citizens and administrations. In that context, DataBridges addresses many research challenges such as acquiring (or producing) Open City Data in RDF, data linking and integration, data provenance, and visualization. This position paper describes our efforts within DataBridges to integrate City Data. We provide a brief introduction to EIT ICT Labs, its goals and structures, and how DataBridges fits them. We then detail the activity and some selected results from 2011 - 2012, and plans for 2013. We conclude with some research challenges we plan to adress in the future.

International audience ; The European Union has created the European Institute of Technology (EIT), within which ICT Labs focuses on fostering exchange and new result creation in the sphere of Information and Communication Technology, across the areas of research, higher education, and industrial innovation. ''Digital Cities of the Future'' is an action line (or chapter) of EIT ICT Labs. Within this action line, we coordinated an activity called ''DataBridges: Data Integration for Digital cities'', whose aim is to produce, link, integrate and exploit open data in the Digital Cities data space, for the benefit of both citizens and administrations. In that context, DataBridges addresses many research challenges such as acquiring (or producing) Open City Data in RDF, data linking and integration, data provenance, and visualization. This position paper describes our efforts within DataBridges to integrate City Data. We provide a brief introduction to EIT ICT Labs, its goals and structures, and how DataBridges fits them. We then detail the activity and some selected results from 2011 - 2012, and plans for 2013. We conclude with some research challenges we plan to adress in the future.

International audience ; The European Union has created the European Institute of Technology (EIT), within which ICT Labs focuses on fostering exchange and new result creation in the sphere of Information and Communication Technology, across the areas of research, higher education, and industrial innovation. ''Digital Cities of the Future'' is an action line (or chapter) of EIT ICT Labs. Within this action line, we coordinated an activity called ''DataBridges: Data Integration for Digital cities'', whose aim is to produce, link, integrate and exploit open data in the Digital Cities data space, for the benefit of both citizens and administrations. In that context, DataBridges addresses many research challenges such as acquiring (or producing) Open City Data in RDF, data linking and integration, data provenance, and visualization. This position paper describes our efforts within DataBridges to integrate City Data. We provide a brief introduction to EIT ICT Labs, its goals and structures, and how DataBridges fits them. We then detail the activity and some selected results from 2011 - 2012, and plans for 2013. We conclude with some research challenges we plan to adress in the future.

Chapitre IX ; Au cours de la dernière décennie, de nombreux pays ont lancé d'ambitieux programmes de dossiers médicaux électroniques (Electronic Health Record - EHR) avec pour objectif d'accroître la qualité des soins tout en réduisant les coûts. L'accès ubiquitaire aux données médicales vise à rendre les informations de santé disponibles en toute sécurité n'importe où et n'importe quand, même dans des environnements déconnectés (par exemple, chez le patient). Les solutions existantes d'EHR basées sur des serveurs s'adaptent mal aux situations déconnectées et peinent à apporter les garanties de sécurité attendues par les patients. La solution proposée dans le présent document s'appuie sur un nouveau dispositif matériel combinant un microcontrôleur sécurisé (similaire à une carte à puce) avec une grande mémoire Flash externe sur un facteur de forme de type clé USB. En embarquant le dossier du patient, un système de gestion de bases de données et un serveur Web dans un tel dispositif, il devient possible de gérer en toute sécurité un dossier médical en autonomie totale. Le présent document propose également une nouvelle manière de personnaliser les politiques de contrôle d'accès pour répondre aux attentes des patients en termes de respect de la vie privée, avec une assistance minimale de la part des praticiens. Bien que les deux propositions soient orthogonales, leur combinaison au sein de la même infrastructure apporte une solution nouvelle et convaincante à la construction de dossiers médicaux ubiquitaire sécurisés.

International audience ; Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.

International audience ; Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.

International audience ; Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.

International audience ; Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.

International audience ; Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.

Chapitre IX ; Au cours de la dernière décennie, de nombreux pays ont lancé d'ambitieux programmes de dossiers médicaux électroniques (Electronic Health Record - EHR) avec pour objectif d'accroître la qualité des soins tout en réduisant les coûts. L'accès ubiquitaire aux données médicales vise à rendre les informations de santé disponibles en toute sécurité n'importe où et n'importe quand, même dans des environnements déconnectés (par exemple, chez le patient). Les solutions existantes d'EHR basées sur des serveurs s'adaptent mal aux situations déconnectées et peinent à apporter les garanties de sécurité attendues par les patients. La solution proposée dans le présent document s'appuie sur un nouveau dispositif matériel combinant un microcontrôleur sécurisé (similaire à une carte à puce) avec une grande mémoire Flash externe sur un facteur de forme de type clé USB. En embarquant le dossier du patient, un système de gestion de bases de données et un serveur Web dans un tel dispositif, il devient possible de gérer en toute sécurité un dossier médical en autonomie totale. Le présent document propose également une nouvelle manière de personnaliser les politiques de contrôle d'accès pour répondre aux attentes des patients en termes de respect de la vie privée, avec une assistance minimale de la part des praticiens. Bien que les deux propositions soient orthogonales, leur combinaison au sein de la même infrastructure apporte une solution nouvelle et convaincante à la construction de dossiers médicaux ubiquitaire sécurisés.

International audience ; Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.

Our daily life activity leaves digital trails in an increasing number of databases (commercial web sites, internet service providers, search engines, location tracking systems, etc). Personal digital trails are commonly exposed to accidental disclosures resulting from negligence or piracy and to ill-intentioned scrutinization and abusive usages fostered by fuzzy privacy policies. No one is sheltered because a single event (e.g., applying for a job or a credit) can suddenly make our history a precious asset. By definition, access control fails preventing trail disclosures, motivating the integration of the Limited Data Retention principle in legislations protecting data privacy. By this principle, data is withdrawn from a database after a predefined time period. However, this principle is difficult to apply in practice, leading to retain useless sensitive information for years in databases. In this paper, we propose a simple and practical data degradation model where sensitive data undergoes a progressive and irreversible degradation from an accurate state at collection time, to intermediate but still informative degraded states, up to complete disappearance when the data becomes useless. The benefits of data degradation is twofold: (i) by reducing the amount of accurate data, the privacy offence resulting from a trail disclosure is drastically reduced and (ii) degrading the data in line with the application purposes offers a new compromise between privacy preservation and application reach. We introduce in this paper a data degradation model, analyze its impact over core database techniques like storage, indexation and transaction management and propose degradation-aware techniques.