Efficient and Secure Data Trading Scheme Based on Blockchain

In the era of digital society, data has emerged as both the cornerstone of computational social systems and the key enabler for social value creation. The development of trustworthy data trading paradigms plays a pivotal role in facilitating data-driven social analysis and collective decision-making processes. Since data can be easily copied, transmitted, and tampered with, data trading has problems such as difficulties in confirming rights, tracing sources, and monitoring. Existing data trading schemes utilize blockchain to trace trading process, decentralized identifier (DID) to achieve autonomous control of user identity, and verifiable credential (VC) to determine data rights. However, existing schemes still have the following problems. First, most of schemes are targeted at small-scale data. Second, the possibility of user identity fraud and abuse still exists. Third, it is difficult to coordinate between VC and data trading. Fourth, existing schemes text colored lack supervision mechanisms. To solve these issues, first, we design a secure data trading (SDT) scheme. The scheme separates data and data rights, which provides a new idea for efficient and reliable trading of larger-scale enterprise data. Second, we design an identity control mechanism. The mechanism effectively improves the credibility of user identity through real-name verification. Third, we design a secure data rights circulation mechanism. The mechanism can quickly determine the rights of trading data and carry out automated verification, which solves the problem of coordination between credential and data trading. Fourth, we design a trading supervision mechanism. The mechanism stores behavioral data on blockchain, which achieves effective rights maintenance of data trading. Finally, based on product carbon footprint scenario, the designed data trading scheme is simulated and verified. The results show that it is secure and feasible to use the data trading scheme proposed for data trading, and it can be used to significantly enhance the quality and reliability of social computing research.