Privacy-Preserved Federated Learning for Autonomous Driving

【Author】 Li, Yijing; Tao, Xiaofeng; Zhang, Xuefei; Liu, Junjie; Xu, Jin

【Source】IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

【影响因子】9.551

【Abstract】In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of concern. The privacy problem is even more severe in autonomous driving business than the other businesses in VEC such as ordinary navigation. Federated learning (FL), which is a privacy-preserved strategy proposed by Google, has become a hot trend to solve the privacy problem in many fields including VEC. Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter only with the help of MEC server. Moreover, different from the common assumption of honest MEC server and honest vehicle in former studies, we take the malicious MEC servers and malicious vehicles into account. First, we consider honest-hut-curious MEC server and malicious vehicles and propose a traceable identity-based privacy preserving scheme to protect the vehicular message privacy where improved Dijk-Gentry-Halevi-Vaikutanathan (DGHV) algorithm is proposed and a blockchain-based Reputation-based Incentive Autonomous Driving Mechanism (RIADM) is adopted. Further, when the case comes to the non-credibility of both parties where semi-honest. MEC server and malicious vehicles are considered, we propose an anonymous identity-based privacy preserving scheme to protect the identity privacy of vehicles with Zero-Knowledge Proof (ZKP). Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss of autonomous driving, increase the accuracy to around 5.55 % while keeps effective privacy of message and identity under the threat of dishonest MEC server and vehicles.

【Keywords】Autonomous driving; federated learning; homomorphic encryption; privacy; vehicular edge computing

【发表时间】2022 JUL

【收录时间】2022-10-16

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习