  The popularity of cyber-physical systems is fueling the rapid growth of
location-based services. This poses the risk of location privacy disclosure.
Effective privacy preservation is foremost for various mobile applications.
Recently, geo-indistinguishability and expected inference error are proposed
for limiting location leakages. In this paper, we argue that personalization
means regionalization for geo-indistinguishability, and we propose a
regionalized location obfuscation mechanism called DPIVE with personalized
utility sensitivities. This substantially corrects the differential and
distortion privacy problem of PIVE framework proposed by Yu et al. on NDSS
2017. 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 Protection Location Set (PLS). In Phase II, we construct a
probability distribution matrix in which the rows corresponding to the same PLS
have their own sensitivity of utility (PLS diameter). Moreover, by designing
QK-means algorithm for more search space in 2-D space, we improve DPIVE with
refined location partition and present fine-grained personalization, enabling
each location to have its own privacy level endowed with a customized privacy
budget. Experiments with two public datasets demonstrate that our mechanisms
have the superior performance, typically on skewed locations.
