Regionalized location obfuscation mechanism with personalized privacy levels
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Global Positioning Systems are now a standard module in mobile devices, and their ubiquity is fueling the rapid growth of location-based services (LBSs). This poses the risk of location privacy disclosure. Effective location privacy preservation is foremost for various mobile applications. Recently two strong privacy notions, geo-indistinguishability and expected inference error, are proposed based on statistical quantification. They are shown to be complementary for limiting the leakage of location information. In this paper, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism with personalized utility sensitivities. This substantially corrects the differential privacy problem of PIVE framework proposed by Yu, Liu and Pu on ISOC Network and Distributed System Security Symposium (NDSS) in 2017. Since PIVE fails to provide differential privacy guarantees on adaptive protection location set (PLS) as pointed in our previous work, we develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the common PLS. In Phase II, we construct a probability distribution matrix by exponential mechanism in which the rows corresponding to the same PLS have their own sensitivity of utility (diameter of PLS). Moreover, we improve DPIVE with refined location partition and fine-grained personalization, in which each location has its own privacy level on two privacy control knobs, minimum inference error and differential privacy parameter. Experiments with two public datasets demonstrate that our mechanisms have the superior performance typically on skewed locations.
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