[2603.00342] Challenges in Enabling Private Data Valuation

Computer Science > Cryptography and Security arXiv:2603.00342 (cs) [Submitted on 27 Feb 2026] Title:Challenges in Enabling Private Data Valuation Authors:Yiwei Fu, Tianhao Wang, Varun Chandrasekaran View a PDF of the paper titled Challenges in Enabling Private Data Valuation, by Yiwei Fu and 2 other authors View PDF HTML (experimental) Abstract:Data valuation methods quantify how individual training examples contribute to a model's behavior, and are increasingly used for dataset curation, auditing, and emerging data markets. As these techniques become operational, they raise serious privacy concerns: valuation scores can reveal whether a person's data was included in training, whether it was unusually influential, or what sensitive patterns exist in proprietary datasets. This motivates the study of privacy-preserving data valuation. However, privacy is fundamentally in tension with valuation utility under differential privacy (DP). DP requires outputs to be insensitive to any single record, while valuation methods are explicitly designed to measure per-record influence. As a result, naive privatization often destroys the fine-grained distinctions needed to rank or attribute value, particularly in heterogeneous datasets where rare examples exert outsized effects. In this work, we analyze the feasibility of DP-compatible data valuation. We identify the core algorithmic primitives across common valuation frameworks that induce prohibitive sensitivity, explaining why straightf...