Towards privacy-preserving location-based services

The advent of personal devices equipped with positioning, computational and communication capabilities, such as smartphones, has led to the large scale collection of the mobility data of individuals and the emergence of Location-Based Services (LBSs), which are personalized according to the position of the users. Examples of innovative LBSs include the search for neighboring services around the user, carpooling application dynamically matching a driver with a potential passenger or real-time traffic monitoring based on information sensed through users' smartphones, just to name a few.

However among all the personal data, learning the location of an individual is one of the greatest threat against his privacy. In particular, the mobility data of an individual can be used to learn the points of interests characterizing his mobility such as his home and place of work, to predict his past, current and future locations or even to discover his social network. In this talk, I will illustrate these risks by demonstrating how it is possible through inference attacks working on mobility traces to deduce other type of personal data. I will also discuss the protection mechanisms that can be used to mitigate these risks and to build privacy-preserving LBSs