RADAR: a Radio-based Analytics for Dynamic Association and Recognition of pseudonyms in VANETs

TL;DR

RADAR introduces a radio-based tracking algorithm that combines DSRC and Wi-Fi signals to de-anonymize vehicles in VANETs, outperforming existing methods and enhancing vehicle tracking in privacy-preserving scenarios.

Contribution

The paper presents a novel radio-based tracking algorithm that effectively combines multiple radio signals to break pseudonym schemes in VANETs, with comprehensive experimental validation.

Findings

Pearson RSSI metric outperforms others in vehicle tracking.

Combining DSRC and Wi-Fi signals improves de-anonymization.

RADAR achieves better tracking accuracy than previous approaches.

Abstract

This paper presents RADAR, a tracking algorithm for vehicles participating in Cooperative Intelligent Transportation Systems (C-ITS) that exploits multiple radio signals emitted by a modern vehicle to break privacy-preserving pseudonym schemes deployed in VANETs. This study shows that by combining Dedicated Short Range Communication (DSRC) and Wi-Fi probe request messages broadcast by the vehicle, it is possible to improve tracking over standard de-anonymization approaches that only leverage DSRC, especially in realistic scenarios where the attacker does not have full coverage of the entire vehicle path. The experimental evaluation compares three different metrics for pseudonym and Wi-Fi probe identifier association (Count, Statistical RSSI, and Pearson RSSI), demonstrating that the Pearson RSSI metric is better at tracking vehicles under pseudonym-changing schemes in all scenarios and against previous works. As an additional contribution to the state-of-the-art, we publicly release all implementations and simulation scenarios used in this work.