Xiang, ZebinCheng, JiujunLiu, CongMao, QichaoYuan, GuiyuanGao, ShangceGao, Shangce2025-12-052025-072327-4662PURE: 133047286PURE UUID: 361d89c4-6ba4-40de-bc07-853a014134ecScopus: 105002394953WOS: 001515514100001http://hdl.handle.net/10362/191551Xiang, Z., Cheng, J., Liu, C., Mao, Q., Yuan, G., Gao, S., & Gao, S. (2025). Privacy-Preserving Autonomous Vehicle Group Formation in a Collusive Attack Scenario. IEEE Internet of Things Journal, 12(13), 25576-25586. https://doi.org/10.1109/JIOT.2025.3559151 --- This work was supported in part by NSFC under Grant 62272344.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.1116281527engAutonomous vehicle groupcollusive attacksfederated learningprivacy-preservationSignal ProcessingInformation SystemsHardware and ArchitectureComputer Science ApplicationsComputer Networks and Communicationsjournal article10.1109/JIOT.2025.3559151https://www.scopus.com/pages/publications/105002394953https://www.webofscience.com/wos/woscc/full-record/WOS:001515514100001