【Author】
Peng, Yongqiang; Chen, Zongyao; Chen, Zexuan; Ou, Wei; Han, Wenbao; Ma, Jianqiang
【Source】MOBILE INFORMATION SYSTEMS
【Abstract】Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms.
【摘要】车联网（IoV）的应用使人类的生活更加智能和便捷。但目前车联网存在数据孤岛、隐私保护不力等问题。为了解决 IoV 中的挑战，我们提出了一个基于区块链的联邦学习池 (BFLP) 框架。 BFLP 允许在不共享原始数据的情况下训练模型，并且可以根据实际应用场景选择最合适的联邦学习方法。考虑到车载系统计算能力较差，我们构建了一种轻量级的加密算法CPC来保护隐私。为了验证所提出的框架，我们在避障和交通预测场景中进行了实验。结果表明，该框架能够有效保护用户隐私，与传统机器学习技术相比更加稳定高效。此外，我们将 CPC 算法与其他加密算法进行了比较。结果表明，与其他对称加密算法相比，其计算成本要低得多。
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