Intelligent Protection Method for Personalized Location Privacy in 3D MCS Scenario

MIN Minghui; YE Jun; WEI Xipeng; MIN Bo; LI Shiyin

doi:10.11999/JEIT251237

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MIN Minghui, YE Jun, WEI Xipeng, MIN Bo, LI Shiyin. Intelligent Protection Method for Personalized Location Privacy in 3D MCS Scenario[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251237

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MIN Minghui, YE Jun, WEI Xipeng, MIN Bo, LI Shiyin. Intelligent Protection Method for Personalized Location Privacy in 3D MCS Scenario[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251237

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Intelligent Protection Method for Personalized Location Privacy in 3D MCS Scenario

doi: 10.11999/JEIT251237 cstr: 32379.14.JEIT251237

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62571529, U25A20388, 62371451), Jiangsu Province Basic Research Special Funds (The Natural Science Foundation) (BK20242083), Jiangsu Province Young Scientific and Technological Talent Support Program (JSTJ-2024-039)

Received Date: 2025-11-24
Accepted Date: 2026-04-08
Rev Recd Date: 2026-04-01

Available Online: 2026-04-22

Abstract

Abstract

Objective With the widespread adoption of intelligent mobile devices and growing reliance on location-based services, Mobile CrowdSensing (MCS) systems have become a critical infrastructure for urban sensing and smart city applications. In complex 3D environments such as hospitals and shopping malls, real-time user location data uploaded during task execution can be exploited by untrusted servers or external attackers, resulting in severe privacy risks. Existing location privacy protection methods are largely designed for 2D spaces and rely on fixed privacy budgets, lacking adaptability to dynamic user energy states, personalized privacy requirements, and inference attacks. These limitations hinder the simultaneous optimization of location privacy and service quality in 3D MCS systems. This paper proposes a personalized privacy-protection task assignment mechanism that integrates 3D Geo-Indistinguishability (3DGI) and distortion privacy, enabling dynamic optimization of location perturbation strategies and task allocation in complex 3D environments. Methods A dynamic 3D MCS system model is established, incorporating user energy states, task execution costs, individual privacy preferences, and attacker Bayesian inference behaviors. A reinforcement learning approach is adopted to learn personalized location perturbation strategies through continuous interaction with the environment. Specifically, a Proximal Policy Optimization (PPO)-based mechanism, PPOM, is proposed. It employs an Actor-Critic architecture to operate in a continuous action space for effective policy learning. A utility-driven reward function integrating user privacy feedback and server profit allows the system to optimize privacy protection and economic benefit simultaneously. Results and Discussions Extensive simulations on synthetic and GeoLife datasets demonstrate that PPOM outperforms 3DGI, 3DGI-PPOM, and LEAPER under Single-user Single-task (S-S) and Single-user Multi-task (S-M) scenarios. PPOM achieves superior 3D location privacy protection through personalized perturbation and two-dimensional action space design. Server net profit is maintained at a level comparable to 3DGI-PPOM while system utility is significantly improved, even under high user privacy preferences. LEAPER underperforms due to its 2D-oriented design. Overall, PPOM dynamically balances personalized privacy protection and server economic benefits in complex 3D MCS environments. Conclusions This study presents a reinforcement learning-based mechanism for personalized 3D location privacy protection and task assignment in dynamic MCS systems. Key contributions include: (1) a personalized privacy protection framework integrating 3DGI and distortion privacy, accounting for user energy status, task costs, privacy preferences, and attacker Bayesian inference in real time; (2) a perturbation policy optimization mechanism, PPOM, based on the PPO with an Actor-Critic structure, Gaussian sampling, and advantage-based learning to enhance robustness and stability in continuous high-dimensional action spaces; (3) a privacy-aware task assignment model using inferred locations from perturbed data, with a utility function jointly quantifying privacy protection and server profit, achieving dynamic trade-offs between user privacy and service quality under resource constraints.
- 3D space,
- Mobile crowdsensing,
- Personalized location privacy,
- Deep reinforcement learning

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References(30)

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