Confidential and Protected Disease Classifier using Fully Homomorphic Encryption

TL;DR

This paper presents a secure disease classification system that uses Fully Homomorphic Encryption combined with deep learning to protect patient privacy during diagnosis, maintaining high accuracy despite encryption constraints.

Contribution

It introduces a novel framework integrating FHE and deep neural networks for private disease diagnosis, including a faster ciphertext summation algorithm.

Findings

Achieves high accuracy with encrypted data

Ensures strict privacy and security

Demonstrates efficiency with a new ciphertext summation method

Abstract

With the rapid surge in the prevalence of Large Language Models (LLMs), individuals are increasingly turning to conversational AI for initial insights across various domains, including health-related inquiries such as disease diagnosis. Many users seek potential causes on platforms like ChatGPT or Bard before consulting a medical professional for their ailment. These platforms offer valuable benefits by streamlining the diagnosis process, alleviating the significant workload of healthcare practitioners, and saving users both time and money by avoiding unnecessary doctor visits. However, Despite the convenience of such platforms, sharing personal medical data online poses risks, including the presence of malicious platforms or potential eavesdropping by attackers. To address privacy concerns, we propose a novel framework combining FHE and Deep Learning for a secure and private diagnosis system. Operating on a question-and-answer-based model akin to an interaction with a medical practitioner, this end-to-end secure system employs Fully Homomorphic Encryption (FHE) to handle encrypted input data. Given FHE's computational constraints, we adapt deep neural networks and activation functions to the encryted domain. Further, we also propose a faster algorithm to compute summation of ciphertext elements. Through rigorous experiments, we demonstrate the efficacy of our approach. The proposed framework achieves strict security and privacy with minimal loss in performance.