Amplifying the Privacy Protection for the Distributed, Structured, and Concerted Data in the Healthcare

Machine learning has gained significant importance in healthcare for enhancing decision- making and improving predictions. However, healthcare data is often distributed across various locations, such as multiple hospitals, which presents a challenge in analyzing this data while protecting sensitive personal information. A new approach ensures privacy during data analysis. We describe the implementation of this method and evaluate its performance using medical datasets. Our method demonstrates how machine learning can be applied to sensitive healthcare data while protecting and safeguarding the privacy.