Differentially Private Training in Tabular GANs: Utility-Privacy Trade-offs

Abstract

Abstract: Privacy risks in differentially private (DP) systems increase significantly when data is correlated, as standard DP metrics often underestimate the resulting privacy leakage, leaving sensitive information vulnerable. Given the ubiquity of dependencies in real-world databases, this oversight poses a critical challenge for privacy protections. Bayesian differential privacy (BDP) extends DP to account for these correlations, yet current BDP mechanisms indicate notable utility loss, limiting its adoption. In this work, we address whether BDP can be realistically implemented in common data struct

Research Question

How do differentially private training techniques in tabular GANs affect the trade-off between synthetic data utility and privacy guarantees, as measured by statistical fidelity metrics like KLD, JS divergence, and sample quality?

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Provenance