Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations

TL;DR

This work tackles trajectory privacy in location-based services for VANETs where temporal correlations can reveal sensitive user behavior. It introduces PTPPM, a personalized mechanism that combines temporal modeling with geo-indistinguishability and distortion privacy, using a Hilbert-curve based search to identify a protection location set and a Permute-and-Flip perturbation to minimize location perturbation while maintaining QoS. The method allows per-user privacy customization through the privacy budget $\epsilon$ and a target inferential error $E_m$, and it shows improved privacy under the same QoS loss compared to prior approaches such as PIVE. Empirical results demonstrate that PTPPM provides stronger protection against posterior location inference while preserving service quality, highlighting its practical potential for privacy-preserving LBS in dynamic vehicular environments.

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.

Figures (6)

• Figure 1: Illustration of the trajectory privacy protection.
• Figure 2: User map coordinates and status coordinates.
• Figure 3: The framework of PTPPM.
• Figure 4: Simulation setting of the trajectory of a user.
• Figure 5: Impact of $\epsilon$ and $E_m$ on personalized trajectory privacy protection.