Protecting Location Privacy Through Crowd Collaboration

Location-based services (LBSs) enable users to sense their surroundings at the risk of exposing coordinates to attackers. Worse yet, a strong adversary with arbitrary knowledges probably derive more privacy especially in continuous query scenarios. To address the problems, a multi-player privacy game mechanism is proposed to satisfy users’ location privacy against adaptive attacks while maximizing utility, building upon which a heuristic algorithm is applied to iteratively converge to the optimal equilibrium point. The gain stems from the collaboration of mobile devices: users share information and forward queries for each other. We evaluate our mechanism against the Bayesian localization attack and maximum possible moving speed attack. The simulations with real map data and mobility traces indicate that our mechanism is effective to preserve privacy at an acceptable price of utility and time complexity.