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

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 2s even for massive datasets with 10^6 records. With a commitment-to-challenge latency maintained at ∼ 1 s on the Sui blockchain, Block-Sampling significantly outperforms prior methods in balancing security with industrial-scale throughput.