Ensuring Privacy and Security of EHR With Secure Collaborative Transfer Learning (SCTL)
D. Dhinakaran, Sundararajan Edwin Raja, A. Ramathilagam, J. Jeno Jasmine, S. Suganya · Advances in healthcare information systems and administration book series · 2024
In the ever-evolving landscape of healthcare, preservation of electronic health records (EHRs) has become paramount. The sensitivity of healthcare data, around personal, medical, and financial information, necessitates vigorous privacy and security measures to prevent breaches that could lead to distinctiveness theft, financial loss, and compromised patient care. This chapter discourses the critical challenge of ensuring data privacy and security in healthcare by leveraging advanced generative AI techniques. Specifically, we present and explore Secure Collaborative Transfer Learning (SCTL), a novel algorithm that syndicates the strengths of Transfer Learning and Federated Learning. The key objective is to improve the security and privacy of EHRs while maintaining high model performance and obedience with regulatory standards. The performance assessment of SCTL demonstrates its usefulness in maintaining high levels of data privacy and security without conciliation on model accuracy or operational effectiveness.