Inference attacks on geolocated data

In recent years, we have observed the development of connected and nomad devices such as smartphones, tablets or even laptops allowing individuals to use location-based services (LBSs), which personalize the service they offer according to the positions of users, on a daily basis. Nonetheless, LBSs raise serious privacy issues, which are often not perceived by the end users. In this thesis, we are interested in the understanding of the privacy risks related to the dissemination and collection of location data. To address this issue, we developed inference attacks such as the extraction of points of interest (POI) and their semantics, the prediction of the next location as well as the de-anonymization of mobility traces, based on a mobility model that we have coined as mobility Markov chain. Afterwards, we proposed a classification of inference attacks in the context of location data based on the objectives of the adversary. In addition, we evaluated the effectiveness of some sanitization measures in limiting the efficiency of inference attacks. Finally, we have developed a generic platform called GEPETO (for GEoPrivacy Enhancing Toolkit) that can be used to test the developed inference attacks.