Section: Scientific Foundations

Privacy

In our world of ubiquitous technologies, each individual constantly leaves digital traces related to his activities and interests which can be linked to his identity. In forthcoming years, the protection of privacy is one of the greatest challenge that lies ahead and also an important condition for the development of the Information Society. Moreover, due to legality and confidentiality issues, problematics linked to privacy emerge naturally for applications working on sensitive data, such as medical records of patients or proprietary datasets of enterprises. Privacy Enhancing Technologies (PETs) are generally designed to respect both the principles of data minimization and data sovereignty. The data minimization principle states that only the information necessary to complete a particular application should be disclosed (and no more). This principle is a direct application of the legitimacy criteria defined by the European data protection directive (Article 7). The data sovereignty principle states that data related to an individual belong to him and that he should stay in control of how this data is used and for which purpose. This principle can be seen as an extension of many national legislations on medical data that consider that a patient record belongs to the patient, and not to the doctors that create or update it, nor to the hospital that stores it. In the CIDRE project, we will investigate PETs that operate at the three different levels (node, set of nodes or open distributed system) and are generally based on a mix of different foundations such as cryptographic techniques, security policies and access control mechanisms just to name a few. Examples of domains where privacy and utility aspects collide and that will be studied within the context of CIDRE include: identity and privacy, geo-privacy, distributed computing and privacy, privacy-preserving data mining and privacy issues in social networks. Here are some concrete examples of our research goals in the privacy field:

• at the node level, we aim at designing privacy preserving identification scheme, automated reasoning on privacy policies  [51] , and policy-based adaptive PETs.

• at the set of nodes level, we plan to augment distributed algorithms (i.e., consensus) with privacy properties such as anonymity, unlinkability, and unobservability.

• at the open distributed system level, we plan to target both geo-privacy concerns (that typically occur in geolocalized systems) and privacy issues in social networks. In the former case, we will adopt a sanitization approach while in the latter one we plan to define privacy policies at user level, and their enforcement by all the intervening actors (e.g, at the social network sites providers).