Emerging ubiquitous applications generally imply the use of geo-located devices (e.g., cell-phones, vehicles) that are aware of their physical location. These objects are often personal devices, so learning their location usually implicitly discloses the location of their owner. Of the various sorts of personal data whose collection can be a threat to privacy, location information is one of the most sensitive: it can help to trace a person, to identify that person's interests or to detect an unusual behavior. It is thus crucial to protect the past, current and future locations of an individual from disclosure (except by explicit consent of the person concerned).

The approach we favor is to reduce location accuracy to reach an acceptable tradeoff between the utility of location-sensitive tasks and privacy protection. Some cryptographic techniques (e.g., secure multiparty computation) can also compute a global result depending on a large number of people without disclosing any information on particular individuals.

To this aim we develop a toolkit to evaluate the trade-off between location accuracy and privacy. In this toolkit, one can import new attacks algorithm and new sanitization techniques, in order to evaluate this trade-off.

Contact: Marc-Olivier Killijian