Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations
CoRR(2024)
摘要
Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer
users numerous conveniences. However, the extensive use of LBSs raises concerns
about the privacy of users' trajectories, as adversaries can exploit temporal
correlations between different locations to extract personal information.
Additionally, users have varying privacy requirements depending on the time and
location. To address these issues, this paper proposes a personalized
trajectory privacy protection mechanism (PTPPM). This mechanism first uses the
temporal correlation between trajectory locations to determine the possible
location set for each time instant. We identify a protection location set (PLS)
for each location by employing the Hilbert curve-based minimum distance search
algorithm. This approach incorporates the complementary features of
geo-indistinguishability and distortion privacy. We put forth a novel
Permute-and-Flip mechanism for location perturbation, which maps its initial
application in data publishing privacy protection to a location perturbation
mechanism. This mechanism generates fake locations with smaller perturbation
distances while improving the balance between privacy and quality of service
(QoS). Simulation results show that our mechanism outperforms the benchmark by
providing enhanced privacy protection while meeting user's QoS requirements.
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