Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations

• URL: http://arxiv.org/abs/2401.11225v1
• Date: Sat, 20 Jan 2024 12:59:08 GMT
• Title: Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations
• Authors: Mingge Cao, Haopeng Zhu, Minghui Min, Yulu Li, Shiyin Li, Hongliang Zhang, Zhu Han,
• Abstract summary: This paper proposes a personalized trajectory privacy protection mechanism (PTPPM) We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. 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.
• Score: 37.88484505367802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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