FASDSA: A Flexible Adaptive and Secure Data Sharing Architecture
ZiXuan Wang, Pan Wang, Zhixin Sun, Xiaokang Zhou, Mengyi Fu, Minyao Liu, Xintong Wang, Lu Chen · ACM Transactions on Autonomous and Adaptive Systems · 2024
With the development of Web 3.0 and Metaverse technologies, the ability of autonomous vehicles has been dramatically improved. These technologies have decentralized features that break the traditional data-sharing mode, grant users control over their data, and achieve benefits through data sharing, promoting the widespread circulation of data. To ensure data exchange security, flexibility, and reliability, this paper proposes FASDSA: A Flexible, Adaptive, and Secure Data Sharing Architecture for CAVs with Web 3.0 and Metaverse. This architecture has three advantages: First, it adopts a decentralized, federated learning and CAV role division method, which allows different computational power CAVs to participate in data sharing according to their roles, achieving flexible data privacy protection. Second, it has the ability of tampered model detection based on interpretable analysis, which can effectively ensure that the model is not tampered with. Third, it has a reward mechanism based on work contribution and trust assessment, which uses blockchain technology to ensure the continuous security operation of this architecture. To verify the performance of FASDSA, we used the UNSW-NB15 dataset to conduct three experiments. The experimental results indicate that compared to traditional methods, FASDSA possesses greater flexibility and security while maintaining similar or even superior model performance.