994 research outputs found

    Redescribing Health Privacy: The Importance of Health Policy

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    Current conversations about health information policy often tend to be based on three broad assumptions. First, many perceive a tension between regulation and innovation. We often hear that privacy regulations are keeping researchers, companies, and providers from aggregating the data they need to promote innovation. Second, aggregation of fragmented data is seen as a threat to its proper regulation, creating the risk of breaches and other misuse. Third, a prime directive for technicians and policymakers is to give patients ever more granular methods of control over data. This article questions and complicates those assumptions, which I deem (respectively) the Privacy Threat to Research, the Aggregation Threat to Privacy, and the Control Solution. This article is also intended to enrich our concepts of “fragmentation” and “integration” in health care. There is a good deal of sloganeering around “firewalls” and “vertical integration” as idealized implementations of “fragmentation” and “integration” (respective). The problem, though, is that terms like these (as well as “disruption”) are insufficiently normative to guide large-scale health system change. They describe, but they do not adequately prescribe. By examining those instances where: a) regulation promotes innovation, and b) increasing (some kinds of) availability of data actually enhances security, confidentiality, and privacy protections, this article attempts to give a richer account of the ethics of fragmentation and integration in the U.S. health care system. But, it also has a darker side, highlighting the inevitable conflicts of values created in a “reputation society” driven by stigmatizing social sorting systems. Personal data control may exacerbate social inequalities. Data aggregation may increase both our powers of research and our vulnerability to breach. The health data policymaking landscape of the next decade will feature a series of intractable conflicts between these important social values

    Steps towards adaptive situation and context-aware access: a contribution to the extension of access control mechanisms within pervasive information systems

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    L'Ă©volution des systĂšmes pervasives a ouvert de nouveaux horizons aux systĂšmes d'information classiques qui ont intĂ©grĂ© des nouvelles technologies et des services qui assurent la transparence d'accĂšs aux resources d'information Ă  n'importe quand, n'importe oĂč et n'importe comment. En mĂȘme temps, cette Ă©volution a relevĂ© des nouveaux dĂ©fis Ă  la sĂ©curitĂ© de donnĂ©es et Ă  la modĂ©lisation du contrĂŽle d'accĂšs. Afin de confronter ces challenges, differents travaux de recherche se sont dirigĂ©s vers l'extension des modĂšles de contrĂŽles d'accĂšs (en particulier le modĂšle RBAC) afin de prendre en compte la sensibilitĂ© au contexte dans le processus de prise de dĂ©cision. Mais la liaison d'une dĂ©cision d'accĂšs aux contraintes contextuelles dynamiques d'un utilisateur mobile va non seulement ajouter plus de complexitĂ© au processus de prise de dĂ©cision mais pourra aussi augmenter les possibilitĂ©s de refus d'accĂšs. Sachant que l'accessibilitĂ© est un Ă©lĂ©ment clĂ© dans les systĂšmes pervasifs et prenant en compte l'importance d'assurer l'accĂ©ssibilitĂ© en situations du temps rĂ©el, nombreux travaux de recherche ont proposĂ© d'appliquer des mĂ©canismes flexibles de contrĂŽle d'accĂšs avec des solutions parfois extrĂȘmes qui depassent les frontiĂšres de sĂ©curitĂ© telle que l'option de "Bris-de-Glace". Dans cette thĂšse, nous introduisons une solution modĂ©rĂ©e qui se positionne entre la rigiditĂ© des modĂšles de contrĂŽle d'accĂšs et la flexibilitĂ© qui expose des risques appliquĂ©es pendant des situations du temps rĂ©el. Notre contribution comprend deux volets : au niveau de conception, nous proposons PS-RBAC - un modĂšle RBAC sensible au contexte et Ă  la situation. Le modĂšle rĂ©alise des attributions des permissions adaptatives et de solution de rechange Ă  base de prise de dĂ©cision basĂ©e sur la similaritĂ© face Ă  une situation importanteÀ la phase d'exĂ©cution, nous introduisons PSQRS - un systĂšme de rĂ©Ă©criture des requĂȘtes sensible au contexte et Ă  la situation et qui confronte les refus d'accĂšs en reformulant la requĂȘte XACML de l'utilisateur et en lui proposant une liste des resources alternatives similaires qu'il peut accĂ©der. L'objectif est de fournir un niveau de sĂ©curitĂ© adaptative qui rĂ©pond aux besoins de l'utilisateur tout en prenant en compte son rĂŽle, ses contraintes contextuelles (localisation, rĂ©seau, dispositif, etc.) et sa situation. Notre proposition a Ă©tĂ© validĂ© dans trois domaines d'application qui sont riches des contextes pervasifs et des scĂ©narii du temps rĂ©el: (i) les Équipes Mobiles GĂ©riatriques, (ii) les systĂšmes avioniques et (iii) les systĂšmes de vidĂ©o surveillance.The evolution of pervasive computing has opened new horizons to classical information systems by integrating new technologies and services that enable seamless access to information sources at anytime, anyhow and anywhere. Meanwhile this evolution has opened new threats to information security and new challenges to access control modeling. In order to meet these challenges, many research works went towards extending traditional access control models (especially the RBAC model) in order to add context awareness within the decision-making process. Meanwhile, tying access decisions to the dynamic contextual constraints of mobile users would not only add more complexity to decision-making but could also increase the possibilities of access denial. Knowing that accessibility is a key feature for pervasive systems and taking into account the importance of providing access within real-time situations, many research works have proposed applying flexible access control mechanisms with sometimes extreme solutions that depass security boundaries such as the Break-Glass option. In this thesis, we introduce a moderate solution that stands between the rigidity of access control models and the riskful flexibility applied during real-time situations. Our contribution is twofold: on the design phase, we propose PS-RBAC - a Pervasive Situation-aware RBAC model that realizes adaptive permission assignments and alternative-based decision-making based on similarity when facing an important situation. On the implementation phase, we introduce PSQRS - a Pervasive Situation-aware Query Rewriting System architecture that confronts access denials by reformulating the user's XACML access request and proposing to him a list of alternative similar solutions that he can access. The objective is to provide a level of adaptive security that would meet the user needs while taking into consideration his role, contextual constraints (location, network, device, etc.) and his situation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile Geriatric Teams, (ii) Avionic Systems and (iii) Video Surveillance Systems

    Legal Issues about Metadata: Data Privacy vs Information Security

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    International audienceFor the purposes of our work we use the concept of metadata to implement enterprise digital right management mechanisms in an intelligent document environment. Such metadata allow us to firstly define contextual security rules and secondly to ensure the information traceability. However, its use may have legal implications, especially with regard to metadata that can be stored (see personal data, privacy), how it should be stored (see probative value in case of litigation, digital forensics) or computer processing in which it may be involved. Another topical issue is the storage and the processing of data using a service provider: the cloud. We must ensure, however, that this solution does not lead to a loss of information controllability for the company. This article aims to position our work with respect to these legal issues

    Smart Home Personal Assistants: A Security and Privacy Review

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    Smart Home Personal Assistants (SPA) are an emerging innovation that is changing the way in which home users interact with the technology. However, there are a number of elements that expose these systems to various risks: i) the open nature of the voice channel they use, ii) the complexity of their architecture, iii) the AI features they rely on, and iv) their use of a wide-range of underlying technologies. This paper presents an in-depth review of the security and privacy issues in SPA, categorizing the most important attack vectors and their countermeasures. Based on this, we discuss open research challenges that can help steer the community to tackle and address current security and privacy issues in SPA. One of our key findings is that even though the attack surface of SPA is conspicuously broad and there has been a significant amount of recent research efforts in this area, research has so far focused on a small part of the attack surface, particularly on issues related to the interaction between the user and the SPA devices. We also point out that further research is needed to tackle issues related to authorization, speech recognition or profiling, to name a few. To the best of our knowledge, this is the first article to conduct such a comprehensive review and characterization of the security and privacy issues and countermeasures of SPA.Comment: Accepted for publication in ACM Computing Survey

    Artificial Intelligence Accountability of Public Administration

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    This article canvasses the use and regulation of artificial intelligence (AI) in US administrative agencies. It is structured as a reply to the questionnaire circulated in advance of the 2022 International Congress of Comparative Law for purposes of preparing the national reports and the general report on the topic of “Artificial Intelligence Accountability of Public Administration.” In large part, the questionnaire’s point of reference is the comprehensive regulation of AI in the European Union’s proposed AI Act. The US reply, contained in this article, highlights the many lacunae in US regulation of AI, similar to the US’s patchwork approach to data privacy
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