Differentially Private Synthetic Data Generation Using Context-Aware GANs

2024-12-15

BigData Congress [Services Society]

AI Summary

This work addresses the dual challenge of generating synthetic data that simultaneously satisfies differential privacy (DP) requirements—ensuring compliance with GDPR and HIPAA—and preserves domain-specific rule consistency (e.g., drug contraindications, regulatory logic)—critical in healthcare, finance, and security. We propose a constraint-matrix-driven, context-aware discriminator for DP-GANs, the first to jointly encode explicit statistical constraints and implicit domain knowledge into the DP-GAN framework. Built upon the WGAN architecture, our method integrates: (i) constraint-matrix-based modeling of domain rules; (ii) a differentially private discriminator employing gradient clipping and Gaussian noise injection; and (iii) domain-knowledge-guided adversarial loss. Experiments demonstrate robust resistance to membership inference attacks under ε ≤ 2.0, a 38% improvement in clinical plausibility (as measured by expert validation rate), and a 22% average gain in downstream task F1 scores—achieving synergistic optimization of privacy preservation and domain-rule fidelity.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The widespread use of big data across various sectors has brought significant privacy concerns, particularly when sensitive information is shared or analyzed. Regulations like GDPR and HIPAA impose strict controls on handling data, making it difficult to balance the need for insights with privacy requirements. Synthetic data offers a promising solution, enabling the creation of artificial datasets that mirror real-world patterns without exposing sensitive information. For instance, synthetic data can simulate patient records or network flows for training machine learning models to conduct research without violating privacy laws. However, traditional synthetic data generation methods often fail to capture complex, implicit rules that relate different elements of the data and are essential in specific domains like healthcare. While these methods might replicate explicit patterns from the training data, they often overlook domain-specific rules that are not directly stated but are critical for maintaining realism and utility. For example, prescription guidelines, such as avoiding certain medications for patients with specific conditions or preventing harmful drug interactions, may not be explicitly represented in the original data. Synthetic data generated without accounting for these implicit rules can lead to medically inappropriate or unrealistic patient profiles. To address these limitations, we propose a framework called Context-Aware Differentially Private Generative Adversarial Network (ContextGAN). Our framework integrates domain-specific rules using a constraint matrix that explicitly encodes both explicit and implicit domain knowledge. The constraint-aware discriminator evaluates synthetic data against these rules, ensuring the generated data adheres to domain constraints. Furthermore, the discriminator is differentially private, ensuring privacy preservation by protecting sensitive details from the original data. We validate ContextGAN across multiple domains, including healthcare, security, and finance, demonstrating that it produces high-quality synthetic data that respects domain-specific rules while preserving privacy. Our results show that ContextGAN significantly improves the realism and utility of synthetic data by enforcing domain constraints, making it suitable for use in scenarios requiring both compliance with explicit patterns and implicit rules, all under strict privacy guarantees.

Problem

• Generates synthetic data with domain-specific implicit and explicit rules.
• Ensures privacy protection through differential privacy in data synthesis.
• Improves realism and utility of synthetic data across sensitive domains.

Innovation

• Context-aware GAN integrates domain-specific constraint matrix
• Constraint-aware discriminator enforces explicit and implicit rules
• Differentially private generation protects sensitive data details