ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1624955
A Localized Differential Privacy Location Preservation Method Based on Hidden Markov Model in Vehicular Networks
Provisionally accepted
Jiwei Li*
Qiuju Zhang- Henan Logistics Vocational College, Zhengzhou, China
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In the booming development of vehicular networks, the information interaction between vehicles and between vehicles and infrastructure is becoming more and more frequent. Vehicle location data has become one of the core information. However, location data contains a large amount of user privacy, and once leaked, it will seriously threaten user safety and freedom. Therefore, there is an urgent need for a method that can balance privacy protection, data availability and processing efficiency. In this paper, a localized differential privacy location protection method based on Hidden Markov Model (HMM) in vehicular networks is proposed, which addresses the problem of vehicle location privacy protection. The method consists of an HMM-based location continuous privacy protection algorithm and a localized differential privacy perturbation algorithm. The algorithm in this paper introduces the Hidden Markov Model into the field and utilizes its ability to accurately predict the continuous change of vehicle location, which provides a scientific basis for the privacy protection operation. At the same time, it combines the spatial correlation of the location distribution to construct a privacy-protecting security area, which effectively restricts the range of the localized differential privacy perturbation, reduces the error, and improves the data availability while safeguarding privacy. Secondly, this method designs a two-stage localized differential privacy perturbation algorithm to achieve dynamic localized differential privacy protection of vehicle location, which can adapt to the real-time changes of vehicle location data, through the collaboration between the client side and the server side. Through the experiments and analysis of the actual trajectory dataset, the results show that the method has strong privacy protection strength, high data availability and processing efficiency, which verifies its feasibility and effectiveness.
Keywords: Hidden Markov Models, vehicles, Localized differential privacy, Privacy preservation, location security
Received: 08 May 2025; Accepted: 09 Sep 2025.
Copyright: © 2025 Li and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiwei Li, Henan Logistics Vocational College, Zhengzhou, China
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