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

2025-07Artigo científico Acesso embargado
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dc.contributor.authorXiang, Zebin
dc.contributor.authorCheng, Jiujun
dc.contributor.authorLiu, Cong
dc.contributor.authorMao, Qichao
dc.contributor.authorYuan, Guiyuan
dc.contributor.authorGao, Shangce
dc.contributor.authorGao, Shangce
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2025-12-05T21:15:23Z
dc.date.embargoedUntil2027-04-09
dc.date.issued2025-07
dc.descriptionXiang, 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.
dc.description.abstractThe 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.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent16281527
dc.identifier.doi10.1109/JIOT.2025.3559151
dc.identifier.issn2327-4662
dc.identifier.otherPURE: 133047286
dc.identifier.otherPURE UUID: 361d89c4-6ba4-40de-bc07-853a014134ec
dc.identifier.otherScopus: 105002394953
dc.identifier.otherWOS: 001515514100001
dc.identifier.urihttp://hdl.handle.net/10362/191551
dc.identifier.urlhttps://www.scopus.com/pages/publications/105002394953
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001515514100001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectAutonomous vehicle group
dc.subjectcollusive attacks
dc.subjectfederated learning
dc.subjectprivacy-preservation
dc.subjectSignal Processing
dc.subjectInformation Systems
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.subjectComputer Networks and Communications
dc.titlePrivacy-Preserving Autonomous Vehicle Group Formation in a Collusive Attack Scenarioen
dc.typejournal article
degois.publication.firstPage25576
degois.publication.issue13
degois.publication.lastPage25586
degois.publication.titleIEEE Internet of Things Journal
degois.publication.volume12
dspace.entity.typePublication
rcaap.rightsembargoedAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)