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应用科学学报 ›› 2026, Vol. 44 ›› Issue (5): 771-786.doi: 10.3969/j.issn.0255-8297.2026.05.005

• 区块链 • 上一篇    下一篇

基于零知识证明的可信数据质量评估

李熙1,2, 文斌1,2, 王怡蓓1,2   

  1. 1. 海南师范大学 人工智能学院, 海南 海口 571158;
    2. 海南师范大学 数据科学与智慧教育教育部重点实验室, 海南 海口 571158
  • 收稿日期:2026-01-20 发布日期:2026-10-09
  • 通信作者: 文斌，教授，研究方向为区块链可靠性、数据交易安全。E-mail:binwen@hainnu.edu.cn
  • 基金资助:
    国家自然科学基金（No.62362029,No.62462029）

Trusted Data Quality Evaluation Based on Zero-Knowledge Proof

LI Xi1,2, WEN Bin1,2, WANG Yibei1,2   

  1. 1. School of Artificial Intelligence, Hainan Normal University, Haikou 571158, Hainan, China;
    2. Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou 571158, Hainan, China
  • Received:2026-01-20 Published:2026-10-09

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摘要： 针对数据质量评估中常见的数据安全问题，如隐私泄露、评估结果被篡改，以及数据交易中缺乏可靠验证机制等，提出一种基于零知识证明的可信数据质量评估方法，通过zk-SNARK技术，数据提供方可在不公开原始数据的前提下，向验证方证明数据质量达标。同时引入Pedersen承诺机制，可保证数据内容的完整性，确保数据不可篡改；并将评估过程与结果上链存储，进一步提升系统整体的透明度与可信度。实验结果表明，所提方法可对多个数据集实现精准质量评估：在保护隐私的同时，能够通过零知识证明顺利完成验证；在Ethereum链上设计、部署智能合约后，系统运行稳定，Gas消耗减少，计算效率提升。该方法在确保数据质量评估可信性的同时，有效保护了用户隐私，适用于需要高隐私保护的数据交易、联邦学习和数据共享等场景。

关键词: 零知识证明, 区块链, 数据质量评估, 隐私保护, 智能合约

Abstract: To address the common data security problems in data quality evaluation, such as privacy leakage, tampering of evaluation results, and lack of reliable verification mechanisms in data transactions, this paper proposed a trusted data quality evaluation method based on zero-knowledge proof (ZKP). By applying zk-SNARK technology, data providers can prove to the verifiers that the data quality is up to the standard without disclosing the original data. Meanwhile, this paper introduced the Pedersen commitment mechanism to ensure the integrity of data content and prevent data tampering. Additionally, the evaluation process and results were stored on the blockchain to further improve the transparency and credibility of the system. The experimental results show that the proposed method can accurately evaluate the quality of multiple datasets. Furthermore, it can smoothly complete the verification by ZKP while protecting privacy. After designing and deploying the smart contract on the Ethereum chain, the system runs stably, the Gas consumption is reduced, and the computational efficiency is improved. The proposed method can effectively protect user privacy while ensuring the credibility of data quality evaluation, and is applicable to scenarios such as data trading, federated learning, and data sharing that require high privacy protection.

Key words: zero-knowledge proof, blockchain, data quality evaluation, privacy protection, smart contract

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