Blockchain-Based Data Verification and Quality Assessment Framework in Data Trading Systems

In recent years, data trading has become an effective means for obtaining data resources and mitigating the data island phenomenon in fields like big data and artificial intelligence. However, traditional centralized data trading models are hampered by issues such as single-point failures and data leaks. Existing solutions have limitations, like the inability to perform trustworthy data verification and quality assessment before trading. Moreover, trading based on raw data can lead to reselling issues. To tackle these challenges, we introduce a novel framework and protocol that uses Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARKs) for secure and private data verification. Our approach also employs accumulators and other technologies to set new standards for objective data quality assessment. The experimental results indicate that our solution excels in computational efficiency and storage cost, effectively addressing data verification and quality assessment issues in data trading.