Privacy-Preserving Synthetic Data Generation via Differential Privacy and Generative AI: Architectures, Utility Bounds, and Attack Verification

Abstract—The cross-organizational sharing of high-dimensional empirical datasets—such as electronic health records, financial credit transactions, and telecommunications telemetry—is fundamentally constrained by strict data governance legislation (e.g., GDPR, HIPAA, CCPA). Heuristic sanitization methods (such as k-anonymity, l-diversity, and pseudo-anonymization) have repeatedly proven vulnerable to linkage and re-identification attacks. Generative Artificial Intelligence (GenAI)—encompassing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Denoising Diffusion Probabilistic Models (DDPMs)—offers the capability to synthesize structurally authentic surrogate populations. However, unconstrained generative models memorize sensitive training instances within their parameter weights, exposing downstream deployments to Membership Inference Attacks (MIA) and Attribute Disclosure Attacks. Differential Privacy (DP) provides the mathematical standard to guarantee bounded information leakage independent of adversarial prior knowledge. This paper presents an exhaustive, mathematically formal technical investigation and empirical benchmark of Privacy-Preserving Synthetic Data Generation uniting Generative AI with Differential Privacy. We formalize the Renyi Differential Privacy (RDP) accounting framework, model gradient perturbation dynamics under Differentially Private Stochastic Gradient Descent (DP-SGD), and evaluate discrete marginal mechanisms (PrivBayes, PGM) alongside continuous generative models (DP-WGAN, DP-Diffusion). Furthermore, we construct an enterprise benchmarking testbed across three high-dimensional, sensitive datasets (MIMIC-III clinical records, German Credit, and Census PUMS). Our findings demonstrate that while unconstrained generative synthesis suffers a 74.2% membership inference vulnerability, DP-Diffusion governed by (epsilon = 1.0, delta = 10^-5) bounds attacker advantage to near-random guessing (51.8%) while preserving 91.4% downstream machine learning classification utility. Finally, we formulate open challenges in high-dimensional privacy budgeting, multi-modal alignment, and verified auditing frameworks. Index Terms—Differential Privacy (DP), Generative AI, Synthetic Data Generation, DP-SGD, Diffusion Models, Membership Inference Attacks (MIA), Renyi Differential Privacy, Data Governance.