Differentially Private Synthetic Data Generation Using Context-Aware GANs

TL;DR

This paper introduces ContextGAN, a novel differentially private GAN that incorporates domain-specific rules via a constraint matrix, enhancing the realism and utility of synthetic data while ensuring privacy across healthcare, security, and finance.

Contribution

We propose ContextGAN, a context-aware differentially private GAN that encodes explicit and implicit domain rules to generate more realistic synthetic data.

Findings

Improved data realism and utility in healthcare, security, and finance datasets.

Effective enforcement of domain-specific constraints in synthetic data.

Strong privacy guarantees with differential privacy protections.

Abstract

The widespread use of big data across sectors has raised major privacy concerns, especially when sensitive information is shared or analyzed. Regulations such as GDPR and HIPAA impose strict controls on data handling, making it difficult to balance the need for insights with privacy requirements. Synthetic data offers a promising solution by creating artificial datasets that reflect real patterns without exposing sensitive information. However, traditional synthetic data methods often fail to capture complex, implicit rules that link different elements of the data and are essential in domains like healthcare. They may reproduce explicit patterns but overlook domain-specific constraints that are not directly stated yet crucial for realism and utility. For example, prescription guidelines that restrict certain medications for specific conditions or prevent harmful drug interactions may not appear explicitly in the original data. Synthetic data generated without these implicit rules can lead to medically inappropriate or unrealistic profiles. To address this gap, we propose ContextGAN, a Context-Aware Differentially Private Generative Adversarial Network that integrates domain-specific rules through a constraint matrix encoding both explicit and implicit knowledge. The constraint-aware discriminator evaluates synthetic data against these rules to ensure adherence to domain constraints, while differential privacy protects sensitive details from the original data. We validate ContextGAN across healthcare, security, and finance, showing that it produces high-quality synthetic data that respects domain rules and preserves privacy. Our results demonstrate that ContextGAN improves realism and utility by enforcing domain constraints, making it suitable for applications that require compliance with both explicit patterns and implicit rules under strict privacy guarantees.