PRIVACY-PRESERVING SYNTHETIC DATA GENERATION USING GENERATIVE ARTIFICIAL INTELLIGENCE

The rapid advancement of Artificial Intelligence (AI) has transformed the way organizations collect, analyze, and utilize data. However, concerns regarding data privacy, legal regulations, and ethical considerations have limited access to high-quality datasets for research and industrial applications. Generative Artificial Intelligence (Generative AI) offers an innovative solution through synthetic data generation, enabling realistic datasets to be produced without exposing sensitive personal information. Synthetic data preserves statistical characteristics of real-world data while reducing the risk of privacy violations. This paper explores privacy-preserving synthetic data generation techniques based on Generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Large Language Models (LLMs). The study discusses methodologies, privacy-preserving mechanisms, evaluation metrics, practical applications, challenges, and future research directions. The findings indicate that Generative AI has the potential to revolutionize secure data sharing and AI model development while supporting regulatory compliance.