A Privacy-preserving Large-scale Data Marketing System Based on Blockchain and Zero Knowledge Proof for VANETs

In the process of integrating the digital economy with the real economy, a vast and diverse supply of data has emerged. Among these, the exponential growth of data in vehicular ad-hoc networks (VANETs) hold immense commercial value. This further drives the demand for building large-scale data marketing platforms to support trading between vehicles and businesses in order to reduce the cost of local management. However, this must address several challenges related to security and performance, such as fairness, privacy protection, and data delivery efficiency. Therefore, this paper proposes a privacy-preserving large-scale data marketing system (PLDM), aiming to address these challenges. Specifically, this solution is based on blockchain to build a decentralized trusted third party to ensure the fairness of the trading process. In addition, we combine the $\Sigma$-protocol and Merkle tree to prove the validity of both the encryption of data to be traded and the identities of the trading participants. This not only achieves privacy protection for data and identities but also reduces the computational costs for vehicles. We provide the security analysis and experimental evaluation of PLDM. And the results show that PLDM performs well in fairness and privacy protection, supporting efficient delivery of large-scale data and low on-chain computational costs.