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<p>Data elements, the core assets fueling AI and big data, face an inherent conflict between circulation utility and privacy preservation. While blockchain and Zero-Knowledge Proofs (ZKPs) provide a decentralized foundation for trust, existing systems suffer from prohibitive computational overhead when handling large-scale datasets. To address these bottlenecks, a novel entropy-driven probabilistic ZKP-based auditing framework, termed <b>Block-Sampling</b>, is proposed for efficient and secure data circulation. The framework leverages a control-data separation architecture, utilizing high-performance public blockchains as an immutable ledger for data commitments and a decentralized entropy source for generating unpredictable sampling indices. Unlike traditional systems, the ZKP construction and verification are conducted off-chain by a regulatory authority or data consumer, which asynchronously validates the sampling proofs against on-chain random seeds. This decoupling effectively neutralizes sampling bias while bypassing blockchain’s execution limits. Experimental evaluations indicate that the proposed framework achieves a 20x reduction in total computational workload, maintaining a per-sample proving latency of approximately <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{2s}\)</EquationSource> </InlineEquation> even for massive datasets with <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{10}^{\varvec{6}}\)</EquationSource> </InlineEquation> records. With a commitment-to-challenge latency maintained at <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{\sim 1}\)</EquationSource> </InlineEquation>s on the Sui blockchain, Block-Sampling significantly outperforms prior methods in balancing security with industrial-scale throughput.</p>

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Block-Sampling: An Entropy-Driven Probabilistic Zero-Knowledge Verification Framework for Efficient Data Element Trading

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Haihua Li,
  • Jinliang Xu,
  • Yizhong Liu,
  • Bingqi Li,
  • Siyu Liu,
  • Zhenyu Guan,
  • Zian Jin

摘要

Data elements, the core assets fueling AI and big data, face an inherent conflict between circulation utility and privacy preservation. While blockchain and Zero-Knowledge Proofs (ZKPs) provide a decentralized foundation for trust, existing systems suffer from prohibitive computational overhead when handling large-scale datasets. To address these bottlenecks, a novel entropy-driven probabilistic ZKP-based auditing framework, termed Block-Sampling, is proposed for efficient and secure data circulation. The framework leverages a control-data separation architecture, utilizing high-performance public blockchains as an immutable ledger for data commitments and a decentralized entropy source for generating unpredictable sampling indices. Unlike traditional systems, the ZKP construction and verification are conducted off-chain by a regulatory authority or data consumer, which asynchronously validates the sampling proofs against on-chain random seeds. This decoupling effectively neutralizes sampling bias while bypassing blockchain’s execution limits. Experimental evaluations indicate that the proposed framework achieves a 20x reduction in total computational workload, maintaining a per-sample proving latency of approximately \(\varvec{2s}\) even for massive datasets with \(\varvec{10}^{\varvec{6}}\) records. With a commitment-to-challenge latency maintained at \(\varvec{\sim 1}\) s on the Sui blockchain, Block-Sampling significantly outperforms prior methods in balancing security with industrial-scale throughput.