Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.

Area of Science:

• Computer Science
• Medical Informatics
• Cryptography

Background:

• Machine learning (ML) and deep learning (DL) offer personalized medicine solutions.
• Patient data confidentiality regulations hinder clinical adoption of DL.
• Existing privacy-preserving methods often involve significant computational overhead or data loss.

Purpose of the Study:

• To propose and evaluate a novel privacy-preserving deep learning framework for sensitive healthcare data.
• To demonstrate the feasibility of performing computations on encrypted health data without compromising accuracy.
• To assess the applicability of the proposed method in clinical workflows, including medical image analysis and hemodynamic modeling.

Main Methods:

• Implementation of the MORE (Matrix Operation for Randomization or Encryption) fully homomorphic encryption (FHE) scheme.
• Application of MORE-FHE to deep learning models for tasks including MNIST digit recognition, hemodynamic modeling, and X-ray coronary angiography classification.
• Evaluation of model performance and computational overhead on encrypted versus unencrypted data.

Main Results:

• Deep learning models trained and applied on MORE homomorphically encrypted data achieved performance comparable to unencrypted models.
• The proposed method demonstrated feasibility for analyzing encrypted medical images and estimating hemodynamic model outputs.
• The MORE scheme offers a reasonable balance between computational efficiency and utility for specific privacy-preserving applications.

Conclusions:

• The proposed privacy-preserving deep learning approach using MORE-FHE is a viable solution for secure healthcare data analysis.
• This method can overcome regulatory hurdles by enabling DL on sensitive patient information without disclosure.
• Further research into the security implications and optimization of the cryptosystem is warranted for broader clinical adoption.