Privacy-Preserving Autonomous Vehicle Group Formation in a Collusive Attack Scenario

Abstract

The dynamic topologies and sensitive information exchanged among autonomous vehicle groups make them prime targets for attackers. In particular, in a collusive attack scenario, malicious nodes can collaborate to manipulate the trust evaluation system, thereby compromising the security of the entire vehicle group. To handle this limitation, this work proposes a privacy-preserving method for forming autonomous vehicle groups in a collusive attack scenario. First, we introduce a distributed trust evaluation algorithm based on a federated learning topology, which preserves local data privacy while facilitating reliable intervehicle trust computation. Then, we propose a PageRank-based detection mechanism that analyzes the trust propagation network to identify potential collusive attackers. Finally, we present a privacy-preserving method for autonomous vehicle group formation. Experimental results show that our proposed approach significantly improves the security and stability of autonomous vehicle groups compared to existing methods.