  Global Positioning Systems are now a standard module in mobile devices, and
their ubiquity is fueling 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 complementary for
limiting the leakage of location information. In this paper, we continue to
study the differential privacy preservation of location obfuscation mechanism
based on PIVE framework proposed by Yu, Liu and Pu on ISOC Network and
Distributed System Security Symposium (NDSS) in 2017. Since PIVE fails to offer
differential privacy guarantees on adaptive protection location set (PLS) as
claimed, we develop DPIVE, a regionalized location obfuscation mechanism 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 each row has its own sensitivity of utility (diameter of
PLS).This approach utilizes the relationship between two privacy notions based
on the user-defined inference error threshold and the prior knowledge about
user's location. Moreover, we introduce PDPIVE, a personalized privacy
framework, to achieve that 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.
