Road Network-Aware Personalized Trajectory Protection with Differential Privacy under Spatiotemporal Correlations

TL;DR

The paper tackles privacy for spatiotemporal trajectories in LBS by modeling road-network constraints and spatiotemporal correlations to enable personalized trajectory privacy. It introduces PTPPM, which fuses geo-indistinguishability with distortion privacy, uses PPBA to allocate privacy budgets according to location sensitivity, and applies a Permute-and-Flip perturbation within dynamically computed Protection Location Sets to balance privacy and QoS. Key innovations include a directed road-network model, a δ-location set framework, Hilbert-curve-based PLS search with rotation, and a locally executed PF mechanism with 2ε-DP guarantees and sequential composition to 2ε_s overall privacy. Empirical results on T-Drive and GeoLife demonstrate improved privacy protections and QoS tradeoffs compared with benchmarks, highlighting practical impact for personalized LBS privacy under realistic mobility patterns.

Abstract

Location-Based Services (LBSs) offer significant convenience to mobile users but pose significant privacy risks, as attackers can infer sensitive personal information through spatiotemporal correlations in user trajectories. Since users' sensitivity to location data varies based on factors such as stay duration, access frequency, and semantic sensitivity, implementing personalized privacy protection is imperative. This paper proposes a Personalized Trajectory Privacy Protection Mechanism (PTPPM) to address these challenges. Our approach begins by modeling an attacker's knowledge of a user's trajectory spatiotemporal correlations, which enables the attacker to identify possible location sets and disregard low-probability location sets. To combat this, we integrate geo-indistinguishability with distortion privacy, allowing users to customize their privacy preferences through a configurable privacy budget and expected inference error bound. This approach provides the theoretical framework for constructing a Protection Location Set (PLS) that obscures users' actual locations. Additionally, we introduce a Personalized Privacy Budget Allocation Algorithm (PPBA), which assesses the sensitivity of locations based on trajectory data and allocates privacy budgets accordingly. This algorithm considers factors such as location semantics and road network constraints. Furthermore, we propose a Permute-and-Flip mechanism that generates perturbed locations while minimizing perturbation distance, thus balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that our mechanism outperforms existing benchmarks, offering superior privacy protection while maintaining user QoS requirements.

Figures (10)

• Figure 1: Illustration of the trajectory privacy protection.
• Figure 2: User map coordinates and status coordinates.
• Figure 3: Directed graph of the road network.
• Figure 4: The framework of PTPPM.
• Figure 5: Simulation setting of the trajectory of a user.

Theorems & Definitions (6)

• Theorem 1
• Proof 1
• Remark 1
• Theorem 2
• Proof 2
• Remark 2