Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
This addresses privacy risks in financial data synthesis for regulatory compliance, but it is incremental as it builds on existing methods.
The paper tackled the privacy-utility tradeoff in synthetic data generation for financial datasets with class imbalance, finding that novel privacy-preserving implementations of GAN and autoencoder synthesizers provide insights into challenges like data quality and privacy.
We explore the privacy-utility tradeoff of synthetic data generation schemes on tabular financial datasets, a domain characterized by high regulatory risk and severe class imbalance. We consider representative tabular data generators, including autoencoders, generative adversarial networks, diffusion, and copula synthesizers. To address the challenges of the financial domain, we provide novel privacy-preserving implementations of GAN and autoencoder synthesizers. We evaluate whether and how well the generators simultaneously achieve data quality, downstream utility, and privacy, with comparison across balanced and imbalanced input datasets. Our results offer insight into the distinct challenges of generating synthetic data from datasets that exhibit severe class imbalance and mixed-type attributes.