Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation

2026-02-10

AI Summary

This study addresses the challenge of balancing privacy preservation and data utility in synthetic data generation for highly regulated financial tabular datasets characterized by severe class imbalance and mixed data types. The authors systematically evaluate the privacy–utility trade-offs of autoencoders, generative adversarial networks (GANs), diffusion models, and Copula-based methods. To better accommodate the unique characteristics of financial data, they propose novel privacy-enhanced GAN and autoencoder architectures. Their experiments provide the first empirical comparison of these generative models in such settings, revealing significant performance differences and offering both methodological innovations and empirical evidence to support the co-optimization of privacy and utility in synthetic financial data generation under stringent regulatory constraints.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• privacy-utility tradeoff
• synthetic data generation
• financial tabular data
• class imbalance
• data privacy

Innovation

• privacy-preserving synthetic data
• class imbalance
• tabular financial data
• GAN
• autoencoder