Homomorphic Encryption in Healthcare Industry Applications for Protecting Data Privacy

TL;DR

This paper evaluates the practical deployment of Fully Homomorphic Encryption in healthcare, focusing on quality control and neural network diagnostics, analyzing performance, resource needs, and current challenges.

Contribution

It provides an empirical analysis of FHE frameworks in healthcare applications, highlighting progress and obstacles in real-world implementation.

Findings

FHE frameworks are effective for healthcare data privacy.

Resource consumption varies significantly between use cases.

Practical deployment faces technical and performance challenges.

Abstract

Focussing on two different use cases-Quality Control methods in industrial contexts and Neural Network algorithms for healthcare diagnostics-this research investigates the inclusion of Fully Homomorphic Encryption into real-world applications in the healthcare sector. We evaluate the performance, resource requirements, and viability of deploying FHE in these settings through extensive testing and analysis, highlighting the progress made in FHE tooling and the obstacles still facing addressing the gap between conceptual research and practical applications. We start our research by describing the specific case study and trust model were working with. Choosing the two FHE frameworks most appropriate for industry development, we assess the resources and performance requirements for implementing each of the two FHE frameworks in the first scenario, Quality Control algorithms. In conclusion, our findings demonstrate the effectiveness and resource consumption of the two use cases-complex NN models and simple QC algorithms-when implemented in an FHE setting.