使用全同态加密的机密且受保护的疾病分类器
Confidential and Protected Disease Classifier using Fully Homomorphic Encryption
- University at Buffalo, The State University of New York（纽约州立大学布法罗分校）
- IBM Research（IBM研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种结合全同态加密与深度学习的端到端安全疾病诊断框架，通过将神经网络适配到加密域并设计更快的密文求和算法，在保护用户医疗隐私的同时实现极小的性能损失。
AI中文摘要：
随着大语言模型（LLMs）的迅速普及，个人越来越倾向于通过对话式人工智能获取各个领域的初步见解，包括疾病诊断等健康相关咨询。许多用户在咨询医疗专业人员之前，会在 ChatGPT 或 Bard 等平台上寻求可能的病因。这些平台通过简化诊断流程、减轻医疗从业者的巨大工作负担，以及帮助用户避免不必要的就诊从而节省时间和金钱，提供了宝贵的益处。然而，尽管此类平台带来了便利，但在线共享个人医疗数据存在风险，包括恶意平台的存在或攻击者的潜在窃听。为了解决隐私问题，我们提出了一种结合全同态加密（FHE）和深度学习的新型框架，用于构建安全且私密的诊断系统。该系统基于问答模式运行，类似于与医疗从业者的互动，这一端到端安全系统采用全同态加密（FHE）来处理加密的输入数据。鉴于FHE的计算限制，我们将深度神经网络和激活函数适配到加密域中。此外，我们还提出了一种更快的算法来计算密文元素的和。通过严格的实验，我们证明了该方法的有效性。所提出的框架在实现严格的安全性和隐私性的同时，仅带来极小的性能损失。
英文摘要：
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.
