A privacy-preserving and robust aggregation scheme for multi-dimensional data in VANETs

Relying on wireless communication technology, vehicular ad-hoc networks (VANETs) realizes vehicle-to-everything (V2X) communication. It can collect real-time data from vehicles and roads to help drivers deal with emergencies and reduce the risk of accidents. However, these multi-dimensional real-time data might reveal the sensitive information of vehicles. Meanwhile, unreliable real-time data can also cause serious accidents. To combat these challenges, we propose a privacy-preserving and robust aggregation scheme for multi-dimensional data in VANETs. Specifically, we utilize the Chinese remainder theorem (CRT) to convert multidimensional real-time data into a large integer to alleviate the burden of its processing, and an EC-ElGamal homomorphic encryption with dual trapdoor decryption mechanism is used to realize the decryption and data query function both vehicles and traffic management center (TMC) to meet the needs of different entities. In addition, Schnorr signature is applied to prevent data from being tampered with or forged, and a credibility mechanism is proposed to guarantee the validity of the data. Security analysis shows that our scheme can ensure the security of vehicle identity and data privacy, and has the ability to identify data availability. Finally, we conduct a theoretical analysis of performance, and use the simulation framework to evaluate the communication overhead. Compared with other schemes, our scheme reduces computational cost and communication overhead by at most 99.6% and 88%, respectively.