ABSTRACT
Protecting healthcare data privacy is paramount necessitating robust measures and technologies these efforts must balance confidentiality security and the ability to extract insights from data however in healthcare machine learning maintaining collaboration by security put forth challenges overcoming this demands preventative measures against breaches and unauthorized access across systems this study focuses on preserving healthcare data privacy emphasizing safeguards to foster collaboration and surmount obstacles fully homomorphic encryption is proposed as a solution enhancing security confidentiality and computational efficiency this approach aims to secure sensitive health data while enabling collaboration in machine learning compared to existing systems it offers improved accuracy precision and comparable time complexity these advantages underscore its efficacy in maintaining security and confidentiality while optimizing performance.
