Time in privacy preserving LBSs: an overlooked dimension

A new privacy model for Location-Based Services (LBSs) has been recently proposed based on users' footprints-these being a repre-sentation of the amount of time a user spends in a given area. Unfortunately, while the model is claimed to be independent from the specific knowledge of the adversary about users' footprints, we argue that an adversary, that has a more structured knowledge over time, can pose a threat to the privacy guarantees of the model. The major contribution of this paper is to show that time is a relevant dimension that needs to be taken into consideration when investigating LBSs privacy issues. In particular, we show that applying our considerations, user privacy can be violated. We support our claim with analysis and a concrete example. Furthermore, by analyzing a real data set of vehicular traces, we show that the threat is actually present in a real scenario and that its effect on jeopardizing user privacy is relevant.