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Personalized 3D Spatiotemporal Trajectory Privacy Protection with Differential and Distortion Geo-Perturbation

Minghui Min, Yulu Li, Gang Li, Meng Li, Hongliang Zhang, Miao Pan, Dusit Niyato, Zhu Han

TL;DR

This work tackles privacy of 3D spatiotemporal trajectories under attackers who exploit location correlations and height information. It introduces 3DSTPM, a mechanism that fuses 3D-GI with distortion privacy, uses a Window-based Adaptive Privacy Budget Allocation to adapt budgets over time, and perturbs locations via a Permute-and-Flip method within optimally chosen protection sets. The approach dynamically accounts for spatiotemporal correlations, location sensitivity, and predictability to bound cumulative privacy leakage while maintaining QoS. Empirical results show substantial privacy gains over 2D and non-correlation-aware baselines with only moderate QoS overhead, validating the practicality of personalized 3D trajectory privacy in smart-city and indoor-outdoor scenarios.

Abstract

The rapid advancement of location-based services (LBSs) in three-dimensional (3D) domains, such as smart cities and intelligent transportation, has raised concerns over 3D spatiotemporal trajectory privacy protection. However, existing research has not fully addressed the risk of attackers exploiting the spatiotemporal correlation of 3D spatiotemporal trajectories and the impact of height information, both of which can potentially lead to significant privacy leakage. To address these issues, this paper proposes a personalized 3D spatiotemporal trajectory privacy protection mechanism, named 3DSTPM. First, we analyze the characteristics of attackers that exploit spatiotemporal correlations between locations in a trajectory and present the attack model. Next, we exploit the complementary characteristics of 3D geo-indistinguishability (3D-GI) and distortion privacy to find a protection location set (PLS) that obscures the real location for all possible locations. To address the issue of privacy accumulation caused by continuous trajectory queries, we propose a Window-based Adaptive Privacy Budget Allocation (W-APBA), which dynamically allocates privacy budgets to all locations in the current PLS based on their predictability and sensitivity. Finally, we perturb the real location using the allocated privacy budget by the PF (Permute-and-Flip) mechanism, effectively balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that the proposed 3DSTPM effectively reduces QoS loss while meeting the user's personalized privacy protection needs.

Personalized 3D Spatiotemporal Trajectory Privacy Protection with Differential and Distortion Geo-Perturbation

TL;DR

This work tackles privacy of 3D spatiotemporal trajectories under attackers who exploit location correlations and height information. It introduces 3DSTPM, a mechanism that fuses 3D-GI with distortion privacy, uses a Window-based Adaptive Privacy Budget Allocation to adapt budgets over time, and perturbs locations via a Permute-and-Flip method within optimally chosen protection sets. The approach dynamically accounts for spatiotemporal correlations, location sensitivity, and predictability to bound cumulative privacy leakage while maintaining QoS. Empirical results show substantial privacy gains over 2D and non-correlation-aware baselines with only moderate QoS overhead, validating the practicality of personalized 3D trajectory privacy in smart-city and indoor-outdoor scenarios.

Abstract

The rapid advancement of location-based services (LBSs) in three-dimensional (3D) domains, such as smart cities and intelligent transportation, has raised concerns over 3D spatiotemporal trajectory privacy protection. However, existing research has not fully addressed the risk of attackers exploiting the spatiotemporal correlation of 3D spatiotemporal trajectories and the impact of height information, both of which can potentially lead to significant privacy leakage. To address these issues, this paper proposes a personalized 3D spatiotemporal trajectory privacy protection mechanism, named 3DSTPM. First, we analyze the characteristics of attackers that exploit spatiotemporal correlations between locations in a trajectory and present the attack model. Next, we exploit the complementary characteristics of 3D geo-indistinguishability (3D-GI) and distortion privacy to find a protection location set (PLS) that obscures the real location for all possible locations. To address the issue of privacy accumulation caused by continuous trajectory queries, we propose a Window-based Adaptive Privacy Budget Allocation (W-APBA), which dynamically allocates privacy budgets to all locations in the current PLS based on their predictability and sensitivity. Finally, we perturb the real location using the allocated privacy budget by the PF (Permute-and-Flip) mechanism, effectively balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that the proposed 3DSTPM effectively reduces QoS loss while meeting the user's personalized privacy protection needs.

Paper Structure

This paper contains 24 sections, 1 theorem, 33 equations, 9 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. User Model
  5. Attack Model
  6. Trajectory Privacy Notions and Problem Statement
  7. 3D Spatiotemporal Trajectory
  8. Location Transition Probability Matrix
  9. Differential Geo-Perturbation in 3D Space
  10. Distortion Geo-Perturbation (Expected Inference Error)
  11. $\delta$-Location Set
  12. Concepts Related To Privacy Budget Allocation
  13. Problem Statement
  14. Personalized Trajectory Privacy Protection Mechanism
  15. Overview
  16. ...and 9 more sections

Key Result

Theorem 1

The distance between the user's real location $\boldsymbol{x}_t$ and the perturbed location $\boldsymbol{x}_t^{\prime}$ sampled from the possible location set $\varDelta \chi _t$ by the PF mechanism, with probability at least $1-\psi, (0 \leq \psi \leq 1)$ satisfies the following inequality:

Figures (9)

  • Figure 1: Illustration of 3D spatiotemporal trajectory privacy protection, the user moves between three large buildings and requests LBS services in both indoor and outdoor scenarios. To prevent attackers from obtaining the user’s personal information from the obtained trajectory data, the user uploads perturbed locations to the LBS server to obtain the corresponding service.
  • Figure 2: User 3D spatiotemporal trajectory coordinate mapping and coordinate status.
  • Figure 3: The framework of 3DSTPM. 3DSTPM provides personalized trajectory privacy protection and is applicable to various scenarios. It operates in three stages: finding the possible location set, finding the protected location set, and perturbing the real location based on the privacy budget allocated according to the user's location sensitivity at different times.
  • Figure 4: $w$-sliding window.
  • Figure 5: Simulation setting of the trajectory of a user.
  • ...and 4 more figures

Theorems & Definitions (9)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Theorem 1
  • proof
  • Remark 1