TabularMark: Watermarking Tabular Datasets for Machine Learning

arXiv:2406.14841v1 Announce Type: new
Abstract: Watermarking is broadly utilized to protect ownership of shared data while preserving data utility. However, existing watermarking methods for tabular datasets fall short on the desired properties (detectability, non-intrusiveness, and robustness) and only preserve data utility from the perspective of data statistics, ignoring the performance of downstream ML models trained on the datasets. Can we watermark tabular datasets without significantly compromising their utility for training ML models while preventing attackers from training usable ML models …

arxiv cs.cr cs.db cs.lg data datasets machine machine learning non ownership performance perspective protect robustness shared statistics utility watermarking