Data valuation, a critical aspect of data-centric ML research, aims to
quantify the usefulness of individual data sources in training machine learning
(ML) models. However, data valuation faces significant yet frequently
overlooked privacy challenges despite its importance. This paper studies these
challenges with a focus on KNN-Shapley, one of the most practical data
valuation methods nowadays. We first emphasize the inherent privacy risks of
KNN-Shapley, and demonstrate the significant technical difficulties in adapting
KNN-Shapley to accommodate differential privacy (DP). To overcome these
challenges, we introduce TKNN-Shapley, a refined variant of KNN-Shapley that is
privacy-friendly, allowing for straightforward modifications to incorporate DP
guarantee (DP-TKNN-Shapley). We show that DP-TKNN-Shapley has several
advantages and offers a superior privacy-utility tradeoff compared to naively
privatized KNN-Shapley in discerning data quality. Moreover, even non-private
TKNN-Shapley achieves comparable performance as KNN-Shapley. Overall, our
findings suggest that TKNN-Shapley is a promising alternative to KNN-Shapley,
particularly for real-world applications involving sensitive data.

本文研究数据估值中的隐私挑战，以 KNN-Shapley 为重点，提出了 TKNN-Shapley 作为其隐私友好的改进版本，并展示了 TKNN-Shapley 在数据质量识别方面相对于 KNN-Shapley 具有更好的隐私 - 效用权衡。