Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models

arXiv:2406.13012v1 Announce Type: cross
Abstract: The promise of tabular generative models is to produce realistic synthetic data that can be shared and safely used without dangerous leakage of information from the training set. In evaluating these models, a variety of methods have been proposed to measure the tendency to copy data from the training dataset when generating a sample. However, these methods suffer from either not considering data-copying from a privacy threat perspective, not being motivated by recent results in …

arxiv can cs.cr cs.lg data generative generative models index information measure plagiarism privacy privacy risk risk shared stat.ml synthetic synthetic data training