Location Inference under Temporal Correlation

Location Based Services (LBSs) have become increasingly popular in the past decade, allowing mobile users to access location-dependent information and services. To protect user privacy while using LBSs, various Location Privacy Protection Mechanisms (LPPMs) have been proposed that obfuscate users' true locations through random perturbation. However, adversaries can still exploit the temporal correlation between a user's locations in multiple LBS queries to improve inference accuracy. In this paper, we introduce a novel location inference attack that strikes a good balance between inference accuracy and computational complexity by effectively exploiting temporal correlation. Simulation studies using synthetic and real datasets confirm the advantages of our proposed attack.