CryptoGAN: Privacy-Preserving Federated Generative Adversarial Networks With Homomorphic Encryption in Healthcare Systems

The convergence of healthcare and financial technology has driven the adoption of federated learning (FL) for collaborative analysis of sensitive data across distributed systems. However, existing approaches face critical challenges, particularly gradient inversion attacks that can reconstruct raw patient data from shared parameters, compromising clinical confidentiality. While FL integrated with conditional generative adversarial networks (GANs) shows promise for medical applications, sharing generator parameters still poses substantial privacy risks. As AI technologies proliferate and the demand for telemedicine expands, the need for secure medical fintech solutions increases, requiring robust mechanisms to protect shared parameters and medical data. To address these limitations, we propose CryptoGAN, a novel approach that embeds a GAN into the client’s local network, aligns the generator’s output distribution with the feature distribution of local data, and aggregates the client’s information by uploading homomorphically encrypted generator parameters. This approach ensures that not only can leakage of local data features be prevented but also the sensitive medical information embedded in the generator parameters is protected, thus improving the privacy of medical applications. Extensive experiments on medical datasets demonstrated that CryptoGAN effectively protects patient privacy while maintaining high diagnostic accuracy and outperforms traditional FL methods in the healthcare domain.