CPCROK: A Communication-Efficient and Privacy-Preserving Scheme for Low-Density Vehicular Ad Hoc Networks

Junchao Wang, Honglin Li, Yan Sun, Chris Phillips, Alexios Mylonas () and Dimitris Gritzalis
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Junchao Wang: Cybersecurity and Computing Systems Research Lab, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Honglin Li: School of Computer Science, University of St Andrews, St Andrews KY16 9AJ, UK
Yan Sun: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Chris Phillips: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Alexios Mylonas: Cybersecurity and Computing Systems Research Lab, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Dimitris Gritzalis: Department of Informatics, Athens University of Economics & Business, 10434 Athens, Greece

Future Internet, 2025, vol. 17, issue 4, 1-24

Abstract: The mix-zone method is effective in preserving real-time vehicle identity and location privacy in Vehicular Ad Hoc Networks (VANETs). However, it has limitations in low-vehicle-density scenarios, where adversaries can still identify the real trajectories of the victim vehicle. To address this issue, researchers often generate numerous fake beacons to deceive attackers, but this increases transmission overhead significantly. Therefore, we propose the Communication-Efficient Pseudonym-Changing Scheme within the Restricted Online Knowledge Scheme (CPCROK) to protect vehicle privacy without causing significant communication overhead in low-density VANETs by generating highly authentic fake beacons to form a single fabricated trajectory. Specifically, the CPCROK consists of three main modules: firstly, a special Kalman filter module that provides real-time, coarse-grained vehicle trajectory estimates to reduce the need for real-time vehicle state information; secondly, a Recurrent Neural Network (RNN) module that enhances predictions within the mix zone by incorporating offline data engineering and considering online vehicle steering angles; and finally, a trajectory generation module that collaborates with the first two to generate highly convincing fake trajectories outside the mix zone. The experimental results confirm that CPCROK effectively reduces the attack success rate by over 90%, outperforming the plain mix-zone scheme and beating other fake beacon schemes by more than 60%. Additionally, CPCROK effectively minimizes transmission overhead by 67%, all while ensuring a high level of protection.

Keywords: beacon; mix zone; privacy; pseudonym changing; RNN; transmission overhead; VANET (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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