SVDT: A Secure and Verifiable Privacy-Preserving Data Trading Scheme for IoT

The data generated in the Internet of Things (IoT) holds significant transactional and utilitarian value. However, traditional data trading models face numerous challenges, including privacy leaks of raw data and limitations in arbitration. This paper proposes a secure and verifiable privacy-preserving data trading scheme for IoT (SVDT), in which perturbed data is substituted for raw data to fundamentally mitigate the risk of privacy leakage. Secondly, we construct a dual-verification mechanism based on homomorphic encryption and merkle trees to simultaneously achieve verifiability and confidentiality during the trading process. Additionally, smart contract facilitate escrow and automated execution to ensure trading fairness. Finally, a collusion-resistant anonymous arbitration mechanism is designed utilizing ring signatures to sever the association between the arbitrator’s identity and the adjudication result, thereby safe-guarding the anonymity and independence of the arbitration while effectively resolving disputes. Theoretical analysis demonstrates that the scheme effectively achieves data security and trading fairness, with security analysis validating its privacy-preserving properties. Experimental results show a favorable balance between performance overhead and privacy utility. The findings indicate that SVDT scheme provides a practical solution for fair trading involving IoT data.