This Hugging Face Space demonstrates fully homomorphic encryption (FHE) applied to a health prediction model. Users select symptoms which are encrypted client-side before being sent to the server. The model performs inference directly on the encrypted data and returns an encrypted diagnosis that only the user can decrypt. The project, created by Zama, showcases practical privacy-preserving machine learning for sensitive domains like healthcare.
Health Prediction sits in PulseGate's AI & ML category. It focuses on enabling medical predictions on sensitive health data without exposing raw information to the computing server. Health Prediction is an open-source project aimed at privacy researchers and healthcare technologists. Health Prediction is free to use. It ships for the web, and it can be self-hosted.
zama-fhe builds and maintains Health Prediction, and it first shipped in 2024. Among its 3 catalogued features are Symptom Input, Encrypted Inference, and Privacy-Preserving Diagnosis.
Summary written by a language model from the project’s public pages.
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