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Article | Open Access

A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

Binyong Li1,2,3Liangming Deng1( )Jie Zhang1Xianhui Deng1
School of Cybersecurity (Xin Gu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China
Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu, 610225, China
SUGON Industrial Control and Security Center, Chengdu, 610225, China
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Abstract

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants, this research focuses on addressing the complex supply relationships in the automotive market, improving data sharing and interactions across various platforms, and achieving more detailed integration of data and operations. We propose a trust evaluation permission delegation method based on the automotive industry chain. The proposed method combines smart contracts with trust evaluation mechanisms, dynamically calculating the trust value of users based on the historical behavior of the delegated entity, network environment, and other factors to avoid malicious node attacks during the permission delegation process. We also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path, prevent information leakage caused by malicious node interception, and effectively protect data integrity and privacy. Experimental analysis shows that this method meets the real-time requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain, offering dynamic flexibility in authorization and scalability compared to most existing solutions.

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Computers, Materials & Continua
Pages 407-426

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Cite this article:
Li B, Deng L, Zhang J, et al. A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain. Computers, Materials & Continua, 2025, 82(1): 407-426. https://doi.org/10.32604/cmc.2024.056730

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Received: 29 June 2024
Accepted: 12 October 2024
Published: 31 January 2025
© The Author 2025.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.