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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 By this principle, data is withdrawn from a database after a predefined time period.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.

International audience ; This chapter advocates the convergence between Access Control (AC) models focusing on the granularity of sharing and Digital Right Management (DRM) models focusing on conditional authorizations and obligations. The convergence is also expected in terms of control enforcement considering that both AC and DRM models must be equally protected against any form of tampering and piracy. We capitalize on the democratization of powerful secure chip platforms (e.g., smart cards, secure USB dongles) which can be plugged in a variety of client devices (PC, PDA, cell phones, consumer electronics) to design a new architecture of a trusted access and usage control system. The benefits of the proposed architecture are exemplified in two different contexts: a fair DRM scenario and a healthcare scenario.

International audience ; This chapter advocates the convergence between Access Control (AC) models focusing on the granularity of sharing and Digital Right Management (DRM) models focusing on conditional authorizations and obligations. The convergence is also expected in terms of control enforcement considering that both AC and DRM models must be equally protected against any form of tampering and piracy. We capitalize on the democratization of powerful secure chip platforms (e.g., smart cards, secure USB dongles) which can be plugged in a variety of client devices (PC, PDA, cell phones, consumer electronics) to design a new architecture of a trusted access and usage control system. The benefits of the proposed architecture are exemplified in two different contexts: a fair DRM scenario and a healthcare scenario.

International audience ; Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB key (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB key). This paper presents the basic framework to make this all work intuitively and efficiently.

National audience ; corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed, say, to the prying eyes of some insurance fraudster. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB device (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose and the public data you access. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB device) in which data flows in only one direction: from public to private. This demonstration shows GhostDB's query processing in action.

National audience ; corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed, say, to the prying eyes of some insurance fraudster. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB device (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose and the public data you access. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB device) in which data flows in only one direction: from public to private. This demonstration shows GhostDB's query processing in action.

International audience ; Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed, say, to the prying eyes of some insurance fraudster. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB device (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose and the public data you access. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB device) in which data flows in only one direction: from public to private. This demonstration shows GhostDB's query processing in action.

International audience ; Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB key (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB key). This paper presents the basic framework to make this all work intuitively and efficiently.

International audience ; Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed, say, to the prying eyes of some insurance fraudster. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB device (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose and the public data you access. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB device) in which data flows in only one direction: from public to private. This demonstration shows GhostDB's query processing in action.

International audience ; Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards 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 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 paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.

International audience ; Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards 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 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 paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.

International audience ; Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards 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 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 paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.

International audience ; Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards 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 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 paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.

International audience ; Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards 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 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 paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.

International audience ; Interoperability of railway systems is a major concern in the European Union. Therefore, ERTMS (European Railway Train Management System) has been defined. Nevertheless, ERTMS has to be paired with national track rules. The Perfect project studies the implementation of ERTMS on the French territory. We use an ERTMS 3D Simulator framework which is able to simulate trains, track and communication between the trains and the RBC. The simulator records all events that occur in a sequence of states called trace. Each event is timestamped, so the sequence of events can be temporally analysed. We propose a methodology to analyse the results of a scenario performed on this platform, using temporal properties. One of the strengths of this method is the computation of statistical information on the trace during its analysis, according to a dedicated pattern of property. An automatic analysis of a trace can be used in several domains.In safety, verification of critical temporal property makes sense in ensuring that a program is compliant with its specification. An analysis of an accident or near accident using temporal properties can provide a safety problem detection and a first level diagnosis. It can also be a interesting tool to evaluate the efficiency of an operator according to several criteria such as tiredness, expertise, repetitiveness of an event. ; L'interopérabilité des systèmes ferroviaires est une préoccupation importante de l'Union Européenne qui a défini le système ERTMS (European Railway Train Management System). ERTMS doit cependant être couplé avec les règles nationales. Le projet PERFECT étudie l'implémentation d'ERTMS sur le territoire français. Nous utilisons un simulateur 3D capable de simuler les trains, les voies et les communications entre les trains et le RBC. Le simulateur enregistre tous les événements qui se produisent dans une séquence d'états appelée trace. Chaque événement est daté, ce qui permet de mener des analyses temporelles sur la trace. Nous proposons une ...