Exploring the future of privacy-preserving heart disease prediction: a fully homomorphic encryption-driven logistic regression approach

Abstract

Homomorphic Encryption (HE) offers a revolutionary cryptographic approach to safeguarding privacy in machine learning (ML), especially in processing sensitive healthcare data. This study aims to address the critical issue of privacy-preserving heart disease prediction by developing a novel Homomorphic Encryption-Driven Logistic Regression (HELR) framework, leveraging the Cheon-Kim-Kim-Song (CKKS) encryption scheme. The framework was implemented using the TenSeal and Torch libraries and evaluated on encrypted heart disease datasets with varying polynomial degrees. The study’s design involved applying the HELR model to three healthcare datasets and comparing its performance with Support Vector Machines (SVM). The major findings revealed that the HELR model achieved high accuracy, within 1% to 3% of its non-HE counterpart, while maintaining competitive computational efficiency. Furthermore, the HELR framework demonstrated robust security against various privacy attacks, including poisoning, evasion, member inference, model inversion, and model extraction, at different ML stages. Notably, the HELR model outperformed SVM in terms of accuracy, showcasing its effectiveness for secure healthcare predictions. The results suggest that HE-enhanced models can offer secure, accurate predictions, paving the way for advancements in privacy-preserving healthcare analytics. However, the study identified limitations related to the computational overhead introduced by HE and the scalability of the model for large datasets. Future work will focus on optimizing encryption techniques and exploring parallel processing methods to address these challenges.