Synthetic Data Generation with GenAI for Privacy-Preserving Smart Healthcare IoT Systems

Abstract

The introduction of the Internet of Things (IoT) in the medical field has enabled the real-time monitoring of patients, the individual diagnosis of patients, and better clinical decisions. But, large scale sharing of sensitive health data raises significant privacy and security concerns. GenAI is an emerging approach to the generation of synthetic information that is both statistically useful and that does not compromise patient privacy. The paper discusses the potential of applying GenAI-based synthetic data generation to the smart healthcare IoT ecosystems, with a particular focus on its integration with federated learning to enhance the privacy protection and system security. We suggest a conceptual framework that combines federated learning and GenAI models- e.g., Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to create high-fidelity synthetic datasets to train diagnostic models, simulate clinical scenarios, and reinforce IoT security. The paper also touches on the ethical and regulatory implications of using synthetic data in healthcare, such as the necessity to comply with privacy laws and regulations across the globe, including HIPAA and GDPR. We find that GenAI-based synthetic data can be deployed to reduce privacy risks and facilitate robust cybersecurity practices in smart healthcare IoT systems, and to facilitate privacy-preserving data-driven innovation.