ZK-Value: A Practical Zero-Knowledge System for Verifiable Data Valuation

TL;DR

ZK-Value introduces a scalable zero-knowledge system for data valuation in marketplaces, enabling privacy-preserving, verifiable Shapley-value attribution with practical proof generation times.

Contribution

It presents a fully co-designed architecture combining locality-based valuation primitives, tailored ZKP protocols, and proof-system optimizations for real-world scalability.

Findings

Achieves valuation accuracy within 0.033 AUROC of exact methods.

Generates proofs in seconds to minutes, outperforming previous ZK baselines.

Verification takes under 4.6 seconds on standard datasets.

Abstract

Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.