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Utilizing Fully Homomorphic Encryption to allow for privacy-preserving analysis of sensitive medical data.
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README.md

README.md

Trustless.Health

We present a WebAssembly interpreter with Fully Homomorphic Encryption to power a dapp for zero-knowledge analysis of sensitive medical data.

Today, we are experiencing rapid growth in the amount of medical data collected - full genome sequencing, microbiome data, etc - and this presents an opportunity for machine learning algorithms to significantly improve health care. However, the current services require you to upload your medical data in clear text (unacceptable with regards to privacy) and lacks proper bench-marking. We introduce Trustless.Health, a decentralised and transparent platform for machine analysis of medical data based on top of Ethereum. We also present fhe-wasm, a WebAssembly interpreter with support for full homomorphic encryption, that backs all models on Trustless.Health meaning no user data (including the results of the analysis!) is ever revealed to the model service providers.

This project was started at the ETH Denver Hackaton 2019.

Tech Stack

The core of Trustless.Health is the compute engine which runs all models using fully homomorphic encryption. To make sure the platform would support as many languages as possible an interpreter was written on top of NuCypher's nufhe package. Thereby, it was achieved to execute models compiled for WebAssembly under FHE. In the rust-example directory, we show how a DNA analysis model written in Rust is compiled to WASM and then executed on encrypted input data using fhe-wasm.

The front-end, hosted at https://trustless.health, is a React/Redux webapp written in typescript with ts-lint for strong type-safety. The web app uses axios to query a local Python server, which uses the nufhe package to generate encryption keys as well as encrypting/decrypting of messages. Users should run this locally (see the client-server directory). The webapp is web3 compatible and should work out of the box with Metamask.

Try it out!

  1. Start Ganache and run truffle migrate --reset in the smart-contract directory.
  2. Run python __main__.py in the provider-sever directory.
  3. Run python __main__.py in the client-sever directory.
  4. Run npm start in the webapp directory.
  5. Log into MetaMask and set the RPC to http://localhost:8545.
  6. In the app create an analysis a category and an analysis offering in the "Provider" tab.
  7. Then you can buy and run an analysis on a DNA string!

Note: Currently all created analysis offerings will run the same algorithm.

You can use the following strings to test:

GAAGCGTGAAGGCGTACTGCGGCTG // Risk score 0

GATGAGTGAGGCGGTTCTGTGGATG // Risk score 4

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