  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.
