PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces

TL;DR

PrivaDE is a privacy-preserving protocol enabling secure data utility evaluation in blockchain marketplaces, ensuring data and model confidentiality while supporting efficient, fair transactions.

Contribution

It introduces a novel secure protocol for joint data utility scoring that is model-agnostic, efficient, and suitable for blockchain integration.

Findings

Achieves online runtimes within 15 minutes for large models

Provides strong security against malicious behavior

Enables fair, automated data transactions in decentralized ML ecosystems

Abstract

Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often unwilling to reveal their proprietary assets. We present PrivaDE, a privacy-preserving protocol that allows a model owner and a data owner to jointly compute a utility score for a candidate dataset without fully exposing model parameters, raw features, or labels. PrivaDE provides strong security against malicious behavior and can be integrated into blockchain-based marketplaces, where smart contracts enforce fair execution and payment. To make the protocol practical, we propose optimizations to enable efficient secure model inference, and a model-agnostic scoring method that uses only a small, representative subset of the data while still reflecting its impact on downstream training. Evaluation shows that PrivaDE performs data evaluation effectively, achieving online runtimes within 15 minutes even for models with millions of parameters. Our work lays the foundation for fair and automated data marketplaces in decentralized machine learning ecosystems.