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

ArXi:2510.18109v4 Announce Type: replace-cross 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 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.