Synthetic Data Generation Models for Privacy-Safe AI

Abstract

The increasing demand for high-quality datasets for AI development has raised significant privacy, security, and regulatory concerns, particularly in sensitive domains such as healthcare, finance, and government. Synthetic data generation addresses these challenges by creating artificial datasets that preserve the statistical characteristics of real data while protecting individual privacy. Recent advances in generative AI, including GANs, VAEs, diffusion models, and transformer-based models, have significantly improved the realism and utility of synthetic data. This paper surveys synthetic data generation techniques, privacy-preserving methods, applications, research challenges, and future directions for developing trustworthy AI systems.