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

MIN Minghui¹,
YE Jun¹,
WEI Xipeng¹,
MIN Bo¹,
LI Shiyin^{1
,
,}

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

Funds: Natural Science Foundation of China (62571529, U25A20388, 62371451), Jiangsu Province Basic Research Special Funds (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: 2026-04-08

Available Online: 2026-04-22

Abstract

Abstract

Objective With the widespread deployment of intelligent mobile devices and the growing reliance on location-based services, Mobile Crowdsensing (MCS) systems have become a vital infrastructure for urban sensing and smart city applications. However, in complex 3D environments such as hospitals and shopping malls, the real-time location data uploaded by users during task execution can be exploited by untrusted servers or external attackers, resulting in severe privacy leakage risks. Existing location privacy protection methods are mostly designed for 2D spaces and often rely on fixed privacy budgets, lacking adaptability to users’ dynamic energy status, personalized privacy needs, and the threat of inference attacks. These limitations hinder the optimization of both location privacy protection and service quality in 3D MCS systems. This paper proposes a personalized privacy-protection task assignment mechanism that incorporates 3D Geo-Indistinguishability (3DGI) and distortion privacy, aiming to enable dynamic optimization of location perturbation strategies and task allocation decisions in complex 3D environments. Methods A dynamic 3D MCS system model is constructed, incorporating key factors such as user energy states, task execution costs, individual privacy preferences, and attacker inference behaviors. Based on this model, a reinforcement learning approach is adopted to learn personalized location perturbation strategies through continuous trial-and-error interaction with the environment. Specifically, a Proximal Policy Optimization (PPO)-based mechanism named PPOM is proposed, which employs an Actor-Critic architecture to operate in a continuous action space for effective policy learning. Moreover, a utility-driven reward function integrating user privacy feedback and server-side profit is introduced, allowing the system to optimize both privacy protection and economic benefit through reinforcement learning. Results and Discussions Extensive simulations on synthetic and GeoLife datasets validate the proposed PPOM mechanism compared with 3DGI, 3DGI-PPOM, and LEAPER under S-S and S-M modes. PPOM delivers superior 3D location privacy protection owing to its personalized perturbation framework and dual-dimensional action space. It maintains server net profit comparable to 3DGI-PPOM while significantly boosting system utility, even at high user privacy preferences. LEAPER underperforms due to its 2D-oriented design. Overall, PPOM achieves a dynamic balance between personalized privacy protection and server economic benefits in complex 3D MCS scenarios. Conclusions This paper proposes a reinforcement learning-based mechanism for personalized 3D location privacy protection and task assignment in dynamic MCS systems. The main contributions are summarized as follows: (1) A personalized privacy protection framework is established by integrating 3DGI and distortion privacy theories, incorporating user energy status, task cost, privacy preferences, and attacker inference behaviors in real-time environments; (2) To overcome the limitations of traditional perturbation strategies in adapting to Bayesian inference attacks and dynamic environments, a perturbation policy optimization mechanism, PPOM, based on the Proximal Policy Optimization (PPO) algorithm is introduced. The Actor-Critic structure, combined with Gaussian sampling and advantage-based learning, enhances the robustness and stability of policy training in continuous action spaces with high dimensionality; (3) A privacy-aware task assignment model is developed using inferred locations from perturbed data, and a utility function is designed to jointly quantify privacy protection and server-side profit, achieving dynamic trade-offs between user privacy and service quality under resource constraints.
- 3D space,
- Mobile crowd sensing,
- Personalized location privacy protection,
- Deep reinforcement learning

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References(30)

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