Enhanced Privacy Preservation and Data Storage in Blockchain-Based Electric Vehicle Network

Abstract

Electric and autonomous vehicles (EV and AV) are spreading quickly on the road and producing extensive amounts of data that must be stored and retrieved securely. The ultimate goal of collecting these data is to feed machine learning models and make predictions to improve the traffic system, increase road safety, and save lives. Existing systems impose the problem of privacy leakage, storage space, and operating cost. They are causing panic among data owners and restricting them from sharing their data. This work presents a proposed solution that utilizes blockchain technology to tackle the aforementioned challenges. The proposed approach incorporates differential privacy and data compression methods to address privacy leaks and data storage space difficulties. Additionally, it aims to enhance the resistance of machine learning models against privacy attacks. By leveraging the features of blockchain, data stored on the blockchain becomes temper proof and protected against a single point of failure. Simulation results and numerical analysis show that the proposed methodology reduces data storage space requirements and improves privacy preservation.