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.

翻译：随着大语言模型（LLMs）的迅速普及，人们越来越多地转向对话式AI来获取各领域的初步见解，包括疾病诊断等健康相关咨询。许多用户在就医前会通过ChatGPT或Bard等平台寻求潜在病因。这些平台通过简化诊断流程、减轻医疗从业者的繁重工作负担，并帮助用户避免不必要的就医以节省时间和金钱，展现出显著价值。然而，此类平台虽带来便利，但在线共享个人医疗数据存在风险，包括遭遇恶意平台或攻击者窃听的可能性。为解决隐私问题，我们提出一种融合全同态加密（FHE）与深度学习的安全隐私诊断系统框架。该系统采用类似医患互动的问答模型，通过全同态加密处理加密输入数据，构建端到端安全系统。针对FHE的计算约束，我们将深度神经网络与激活函数适配至加密域，并提出一种更快的密文元素求和算法。通过严谨的实验验证，本框架在严格保障安全与隐私的前提下实现了极低的性能损失。