A Trusted Data Privacy Computing Method for Vehicular Ad Hoc Networks Based on Homomorphic Encryption and DAG Blockchain

Wenxian Jiang, Jun Tao, Zhenglei Guan

Abstract: Recently, vehicular ad hoc networks (VANETs) have garnered significant attention in the industry, thanks to their distinctive characteristics of mobility and real-time capabilities. In order to protect the privacy of data sharing in VANETs, a privacy computing scheme based on homomorphic encryption and blockchain has been proposed. First, the Paillier algorithm of homomorphic encryption is used to encrypt the data, making it available but not invisible to ensure the security of the data. Second, the data structure of a directed acyclic graph (DAG) is applied to replace the chain structure so that it can be processed in parallel and reduce the delay time of transmission confirmation. Finally, a single point cluster is set up in the Bigchain database, and Docker virtual technology is used to simulate the high dynamic and high load environment of VANETs, so as to verify the computational efficiency and security of the proposed scheme. The experimental results show that the privacy computing scheme combining homomorphic encryption and DAG blockchain not only eliminates the need for repeated encryption and decryption in the communication process but also reduces the computational cost associated with private data. Compared with traditional chain blockchain, this scheme achieves a comprehensive delay reduction of 68.51% and shortens the average confirmation time by 85.73%, effectively meeting the high-performance requirements of VANETs.