Abstract

The exponential growth of healthcare data, along with its sensitive nature, has necessitated the development of innovative solutions for protecting patient privacy. Generative AI techniques, such as Generative Adversarial Networks (GANs), have shown promise in creating synthetic healthcare data that mirrors real-world patterns while preserving confidentiality. This paper proposes a privacy-enhanced generative AI framework for the creation of synthetic healthcare data. By incorporating differential privacy and federated learning, the system aims to enhance privacy while maintaining data utility for healthcare research and machine learning tasks. The proposed framework not only safeguards patient information but also enables the creation of diverse, realistic synthetic datasets that can be leveraged for various healthcare applications. Results demonstrate that the synthetic data retains statistical integrity without compromising privacy, making it a viable solution for healthcare data sharing and analysis.