Homomorphic Encryption and Collaborative Machine Learning for Secure Healthcare Analytics

Abstract

With the advent of the Internet of Things (IoT), the conventional healthcare system has evolved into a smart healthcare system, offering intelligent prognosis and diagnosis services. However, as the healthcare sector embraces technological advances, concerns about the privacy and security of critical patient data have become more prevalent. Due to adversarial attacks on traditional machine learning (ML), the security of these intelligent systems is increasingly at risk. Collaborative machine learning (CML) and homomorphic encryption (HE) have recently become viable approaches to circumvent the security challenges of healthcare systems. Inspired by the staggering benefits of CML and HE, this research article examines different cryptographic techniques that enable computations on encrypted data while delving into the fundamental ideas of HE. Simultaneously, it explores various frameworks for CML and highlights their potential for decentralized model training. The paper also critically analyses the benefits and challenges of integrating HE with CML, offering insights into safe model aggregation, guaranteeing data privacy, and performance optimization techniques for use in healthcare environments. Further, we delved into pragmatic scenarios and actual implementations, illustrating how the unified framework can improve diagnosis and cooperative research in smart healthcare systems. Lastly, we presented a case study that evaluates different ML algorithms, such as k‐nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR), to secure healthcare analytics. The results show that KNN had the best accuracy of 76.5%, with RF and SVM having an accuracy of 76%. The accuracy for LR is 73.5%, which is lower than all other models. These findings offer insightful information for selecting models that take accuracy and the trade‐off between precision, recall, and F1 score into account. This helps researchers make well‐informed selections for their classification work in securing healthcare analytics.