Secure Multi-Party Computation for Medical Data Protection

Abstract

The growing digitization of healthcare systems has greatly enhanced the accessibility and efficiency of medical data and diagnosis. Nevertheless, it has also posed significant issues concerning the privacy of data, its security, and compliance with regulations. When stored or processed in centralized systems, sensitive patient data, such as clinical history and diagnostic history, is extremely susceptible to breaches. Historical healthcare machine learning models typically involve combining data from several institutions, which is a serious privacy concern. Secure Multi-Party Computation (SMPC) has become one of the promising cryptographic paradigms that help to cooperatively compute functions on the data of multiple parties without disclosing the sensitive information. This paper introduces a privacy-preserving machine learning model with SMPC to predict heart diseases. The suggested system enables the distributed healthcare institutions to collaboratively train predictive models without providing raw patient information. To test the framework, a heart disease dataset with more than 1000 records of patients was used. Random Forest and Gradient Boosting machine learning models were tested in a simulated SMPC setting. The findings show that the suggested solution has high predictive accuracy and guarantees high levels of data confidentiality. Gradient Boosting attained 88% accuracy, which is better than random forest. The results indicate that SMPC combined with machine learning is able to resolve privacy issues in healthcare analytics without drastically deteriorating the performance of the model. This study serves as part of the expanding body of privacy-sensitive AI and emphasizes the promise of SMPC in privacy-sensitive medical data sharing and collaborative medical research.