Federated learning with blockchain for privacy-preserving data sharing in Internet of vehicles
Wenxian Jiang, Mengjuan Chen, Jun Tao
Abstract
Data sharing technology in Internet of Vehicles(IoV) has attracted great research interest with the goal of realizing intelligent transportation and traffic management. Meanwhile, the main concerns have been raised about the security and privacy of vehicle data. The mobility and real-time characteristics of vehicle data make data sharing more difficult in IoV. The emergence of blockchain and federated learning brings new directions. In this paper, a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in IoV. First, we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of data. Then, we integrate the verification scheme into the consensus process, so that the consensus computation can filter out low-quality models. Experimental data shows that our data-sharing model can better balance the relationship between data availability and privacy, and also has enhanced security.