An Aggregation Protocol Resisting Collusion Attacks in the Internet of Vehicles Environment.

Zisang Xu, Ruirui Zhang, Peng Huang,Jianbo Xu

CSCloud/EdgeCom(2023)

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摘要
In the Internet of Vehicles(IoV) based on federated learning, the vehicle avoids the server from collecting sensitive data of users by uploading model parameters. However, after research, it is found that the model parameters uploaded by the vehicle are also vulnerable to model inversion attacks or other attacks, thus exposing sensitive data of users. Therefore, this paper proposes an aggregation protocol resisting collusion attacks in the Internet of Vehicles environment. First, the Roadside Unit (RSU) and the Trusted Authority (TA) cooperate to issue tokens for the vehicle to reduce the authentication overhead of the vehicle frequently crossing domains. Second, the protocol uses blinding factors and secret sharing techniques to effectively resist collusion attacks between entities. Finally, after mathematical analysis, it is proved that the protocol has high security and efficiency.
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关键词
Internet of Vehicles,Federated learning,Token,Collusion attack,Blinding factor
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