Generative Models with Privacy Guarantees: Enhancing Data Utility while Minimizing Risk of Sensitive Data Exposure

Abstract

The rapid advancement in generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, has significantly enhanced our ability to create high-quality synthetic data. These models have been instrumental in various applications, ranging from data augmentation and simulation to the development of privacy-preserving solutions. However, the generation of synthetic data also raises critical privacy concerns, as there is potential for these models to inadvertently reveal sensitive information about individuals in the original datasets. This paper delves into the intersection of generative models and data privacy, focusing on the development of techniques that safeguard privacy while ensuring the synthetic data produced remains meaningful and useful. We provide a comprehensive review of privacy-preserving strategies employed in the context of generative models. Key approaches discussed include differential privacy, which guarantees that the inclusion or exclusion of any individual data point does not significantly alter the output of a function; federated learning, which enables collaborative model training across decentralized data sources without sharing raw data; and secure multi-party computation (MPC), which allows for computations on encrypted data while preserving privacy. The paper evaluates these techniques in terms of their effectiveness, trade-offs, and integration challenges.