An efficient and secure data collection scheme for predictive maintenance of vehicles

Abstract

Through the real-time evaluation of vehicle operating status, the predictive maintenance operated by cloud servers can detect the abnormal condition of vehicles, and prompt early warning information before vehicle failure, so as to guide vehicle owners to carry out relevant vehicle repair and maintenance to achieve the goal of safe driving. However, vehicles still face data security and privacy preservation issues in the data collection stage of predictive maintenance. To solve this problem, in this paper, we propose an efficient and secure data collection scheme, named ESDC, for predictive maintenance of vehicles. Specifically, ESDC exploits the superincreasing sequence and lightweight homomorphic encryption technique to protect the vehicle’s data content, and adds geometric noise to resist differential attack. In addition, ESDC employs shared secret key and lightweight authentication technology to ensure data integrity and authentication. Security analysis shows that ESDC can reach security requirements, including privacy preservation, differential privacy, data integrity, authentication, and filtering false data. Furthermore, the performance evaluations also show that ESDC is efficient and outperforms the existing approaches.