Preserving Privacy for Discrete Location Information

With the development of smart mobile devices, location privacy has gained attention from both academia and industry. In recent years, a variety of location privacy definitions from different perspectives have been proposed to quantify location privacy and compare location privacy protection mechanisms (LPPMs). These definitions, however, have some drawbacks. In this paper, we propose a location privacy metric for discrete location information which improves the quantification of distance between the prior and posterior distribution of an adversary who may hold background knowledge in differential privacy. Furthermore, we develop a non-convex optimization problem and construct a near-optimal mechanism. We evaluate our proposed metric by comparing it to the state-of-the-art definitions including Shokri's incorrectness, Andrés's geo-indistinguishability and Dong's DPLO. We also evaluate our proposed mechanism with the optimal mechanisms based on the afore mentioned existing definitions. We make experiments on both simulation and realworld dataset, and the results show that our proposed metric and mechanism have the ascendant position.