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

2025-11-25

AI Summary

To address privacy leakage risks arising from spatiotemporal correlations in user trajectories within location-based services (LBS), this paper proposes a personalized location privacy protection mechanism. The method integrates geo-indistinguishability with distortion privacy to construct a road-network-aware, semantics-sensitive sensitivity evaluation model and designs a dynamic privacy budget allocation algorithm. It further introduces the novel Permute-and-Flip perturbation mechanism, which significantly reduces the average perturbation distance while guaranteeing a configurable inference error bound. Experimental evaluations on multiple real-world trajectory datasets demonstrate that the proposed approach improves privacy protection strength by 23.6% on average over baseline methods. Concurrently, it constrains degradation in query service quality—including location availability and response latency—to within 5%, thereby achieving an effective trade-off between privacy preservation and practical utility.

Technology Category

• Machine Learning: Privacy
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Protecting personalized trajectory privacy against spatiotemporal correlation attacks
• Allocating differential privacy budgets based on location sensitivity and semantics
• Balancing privacy protection with service quality through optimized location perturbation

Innovation

• Integrates geo-indistinguishability with distortion privacy framework
• Uses personalized privacy budget allocation based on location sensitivity
• Employs Permute-and-Flip mechanism to minimize location perturbation distance