Privacy-preserving automated exposure notification

Contact tracing is an essential component of public health efforts to slow the spread of COVID-19 and other infectious diseases. Automating parts of the contact tracing process has the potential to significantly increase its scalability and efficacy, but also raises an array of privacy concerns, including the risk of unwanted identification of infected individuals and clandestine collection of privacy-invasive data about the population at large. In this paper, we focus on automating the exposure notification part of contact tracing, which notifies people who have been in close proximity to infected people of their potential exposure to the virus. This work is among the first to focus on the privacy aspects of automated exposure notification. We introduce two privacy-preserving exposure notification schemes based on proximity detection. Both systems are decentralized - no central entity has access to sensitive data. The first scheme is simple and highly efficient, and provides strong privacy for non-diagnosed individuals and some privacy for diagnosed individuals. The second scheme provides enhanced privacy guarantees for diagnosed individuals, at some cost to efficiency. We provide formal definitions for automated exposure notification and its security, and we prove the security of our constructions with respect to these definitions.First author draf

Mathematical practice, crowdsourcing, and social machines

The highest level of mathematics has traditionally been seen as a solitary endeavour, to produce a proof for review and acceptance by research peers. Mathematics is now at a remarkable inflexion point, with new technology radically extending the power and limits of individuals. Crowdsourcing pulls together diverse experts to solve problems; symbolic computation tackles huge routine calculations; and computers check proofs too long and complicated for humans to comprehend. Mathematical practice is an emerging interdisciplinary field which draws on philosophy and social science to understand how mathematics is produced. Online mathematical activity provides a novel and rich source of data for empirical investigation of mathematical practice - for example the community question answering system {\it mathoverflow} contains around 40,000 mathematical conversations, and {\it polymath} collaborations provide transcripts of the process of discovering proofs. Our preliminary investigations have demonstrated the importance of "soft" aspects such as analogy and creativity, alongside deduction and proof, in the production of mathematics, and have given us new ways to think about the roles of people and machines in creating new mathematical knowledge. We discuss further investigation of these resources and what it might reveal. Crowdsourced mathematical activity is an example of a "social machine", a new paradigm, identified by Berners-Lee, for viewing a combination of people and computers as a single problem-solving entity, and the subject of major international research endeavours. We outline a future research agenda for mathematics social machines, a combination of people, computers, and mathematical archives to create and apply mathematics, with the potential to change the way people do mathematics, and to transform the reach, pace, and impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent Computer Mathematics, CICM 2013, July 2013 Bath, U

Inferring Accountability from Trust Perceptions

International audienceOpaque communications between groups of data processors leave individuals out of touch with the circulation and use of their personal information. Empowering individuals in this regard requires sup-plying them — or auditors on their behalf — with clear data handling guarantees. We introduce an inference model providing individuals with global (organization-wide) accountability guarantees which take into account user expectations and varying levels of usage evidence, such as data handling logs. Our model is implemented in the IDP knowledge base system and demonstrated with the scenario of a surveillance infrastructure used by a railroad company. We show that it is flexible enough to be adapted to any use case involving communicating stakeholders for which a trust hierarchy is defined. Via auditors acting for them, individuals can obtain global accountability guarantees, providing them with a trust-dependent synthesis of declared and proven data handling practices for an entire organization

Immunohistochemical localization of certain nervous system markers in retina of Anatolian ground squirrel (Spermophilus xanthoprymnus)

Given the significant amount of personal information available on the Web, verifying its correct use emerges as an important issue. When personal information is published, it should be later used under a set of usage policies. If these policies are not followed, sensitive data could be exposed and used against its owner. Under these circumstances, processing transparency is desirable since it allows users to decide whether information is used appropriately. It has been argued that data provenance can be used as the mechanism to underpin such a transparency. Thereby, if provenance of data is available, processing becomes transparent since the provenance of data can be analysed against usage policies to decide whether processing was performed in compliance with such policies. The aim of this paper is to present a Provenance-based Compliance Framework that uses provenance to verify the compliance of processing to predefined information usage policies. It consists of a provenance-based view of past processing of information, a representation of processing policies and a comparison stage in which the past processing is analysed against the processing policies. This paper also presents an implementation using a very common on-line activity: on-line shopping

Privacy Management in Global Organisations

Part 4: KeynotesInternational audienceMeeting privacy requirements can be challenging for global organisations, particularly where future Internet service provision models are involved. In this paper approaches will be explained that can be used to help address these issues, with a focus on some of the solutions that the author has been involved in developing in HP Labs that are currently being used, rolled out or are the subjects of further research

Optimization and Estimated Pareto Front of the Maximum Lift/Drag Ratio and Roll Stability Coefficient

Given the significant increase of on-line services that require personal information from users, the risk that such information is misused has become an important concern. In such a context, information accountability is desirable since it allows users (and society in general) to decide, by means of audits, whether information is used appropriately. To ensure information accountability, information flow should be made transparent. It has been argued that data provenance can be used as the mechanism to underpin such a transparency. Under these conditions, an audit's quality depends on the quality of the captured provenance information. Thereby, the integrity of provenance information emerges as a decisive issue in the quality of a provenance-based audit. The aim of this paper is to secure provenance-based audits by the inclusion of cryptographic elements in the communication between the involved entities as well as in the provenance representation. This paper also presents a formalisation and an automatic verification of a set of security properties that increase the level of trust in provenance-based audit results