Privacy-Preserving Analytics with Synthetic Data Generation

Abstract

In domains such as healthcare, finance, and telecommunications, the tension between data utility and privacy poses significant challenges. Synthetic data generation offers a compelling solution—creating artificial datasets that emulate real-world distributions while safeguarding individual privacy. This paper explores synthetic data's role in enabling privacy-preserving analytics, drawing exclusively from research prior to 2022. We survey models and frameworks that generate synthetic data with privacy guarantees, particularly those incorporating differential privacy. DP-CGAN is a notable example—a Differentially Private Conditional GAN that leverages Rényi differential privacy to produce labeled, visually coherent outputs on datasets like MNIST while preserving strong privacy guarantees (single-digit epsilon) arXiv. In healthcare contexts, convolutional GANs combined with Rényi differential privacy preserve temporal and structural correlations for synthetic medical data generation arXiv. However, critical evaluation shows that synthetic data doesn't always outperform traditional anonymization methods in balancing privacy and utility—its properties may be unpredictable arXiv. Applications extending beyond healthcare include using synthetic data for bias mitigation—yielding 15–20% bias reduction and 10–12% accuracy improvements with low re-identification risk MDPI. Reviews of federated learning combined with synthetic generation (“federated synthesis”) emphasize the potential for privacy-safe, decentralized data integration across institutions PopData Science Journal. Our proposed methodology integrates DP-aware generative modeling, federated synthesis for cross-institutional privacy, and systematic privacy-utility evaluation. Advantages include scalable privacy protection and adaptability to restricted data settings; disadvantages lie in unpredictable utility outcomes, evaluation variability, and complexity in maintaining faithful real-world correlations. We conclude that synthetic data is a promising privacy-preserving tool—but one requiring rigorous evaluation and cautious application, particularly in sensitive domains. Future work should focus on robust privacy-utility metrics, formal differential privacy integration, and hybrid synthetic-real data workflows to bolster both privacy and analytical validity.