GAN-Based Privacy-Preserving Data Synthesis in Healthcare

Strict privacy laws make it difficult to access high-quality clinical datasets, posing a major challenge for the advancement of artificial intelligence in healthcare. Generative Adversarial Networks (GANs) offer a promising approach for synthesizing realistic healthcare data without exposing sensitive patient information. This paper investigates the use of GAN-based frameworks to generate synthetic medical datasets that preserve both statistical properties and clinical relevance while ensuring patient confidentiality. We review state-of-the-art GAN architectures applied in healthcare, evaluate their performance in terms of privacy protection and data utility, and propose a privacy-aware GAN synthesis framework. Experimental results demonstrate that the proposed approach significantly reduces privacy risks while maintaining data quality suitable for disease prediction and treatment outcome analysis. The findings support safer data sharing and collaborative AI research in the medical domain.

Generative Adversarial Networks (GANs) Healthcare Data Synthesis Privacy Preservation Synthetic Data Medical AI Data Anonymization Differential Privacy EHR Data Generation Deep Learning in Healthcare Data Sharing in Medicine

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