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

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.

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Publication Details

Journal
Future Internet
Published
2025-04-09
DOI
https://doi.org/10.3390/fi17040165
Citations
2
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
1.46
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article

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

Wanjun Li, J. Wang, Alexios Mylonas, Dimitris Gritzalis et al.
2 citations
Future Internet
Vehicular Ad Hoc Networks (VANETs)
1.46
article

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

Wanjun Li, J. Wang, Alexios Mylonas, Dimitris Gritzalis, Yan Sun, Chris Phillips
article en
2 citations

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.

Future InternetVol. 17(4)
University of Hertfordshire (GB), Queen Mary University of London (GB), University of St Andrews (GB), Athens University of Economics and Business (GR)
Openalex Percentile: Top 18%
Vehicular Ad Hoc Networks (VANETs)
1.46
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