Quantifying and Mitigating Privacy Risks for Tabular Generative Models

arXiv:2403.07842v1 Announce Type: cross
Abstract: Synthetic data from generative models emerges as the privacy-preserving data-sharing solution. Such a synthetic data set shall resemble the original data without revealing identifiable private information. The backbone technology of tabular synthesizers is rooted in image generative models, ranging from Generative Adversarial Networks (GANs) to recent diffusion models. Recent prior work sheds light on the utility-privacy tradeoff on tabular data, revealing and quantifying privacy risks on synthetic data. We first conduct an exhaustive empirical analysis, …

adversarial arxiv cs.cr cs.lg data gans generative generative adversarial networks generative models image information networks privacy privacy risks private risks sharing solution synthetic synthetic data technology