Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms

Abstract

The prevalence of mobile devices and Location-Based Services (LBS) necessitates the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBSes due to the noise they add to users’ locations. Here, we consider the remapping technique, which presumes the adversary has a perfect statistical model for the user location. We consider this assumption and show that under practical assumptions on the adversary’s knowledge, the remapping technique leaks privacy not only about the true location data, but also about the statistical model. Finally, we introduce a novel method termed Randomized Remapping to provide a trade-off between leakage of the users’ location and leakage of the users’ model for a given utility.