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Linear regression using FHE gMorph library
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README.md

gudot

This is a showcase for feasibility of gMorph/FHE on Golem. It computes linear regression using FHE gMorph library. The whole software is experimental and is provided for demonstration purposes only. Please note that we make no claims about security of the encryption scheme. It is included in Golem's workshop on DevCon 5.

gudot GIF demo

Requirements

  • emsdk - installation instructions can be found here
  • gwasm-runner - you can either build it from source as described here, or download a precompiled binary from here
  • freetype library - on Ubuntu install package libfreetype6-dev

Building

Assuming you've got the prerequisites satisfied, in order to build the project, simply run

cargo build --release

Running

The project involves executing several steps in sequence: some steps are done on the host OS (so your machine), while the main step is performed using gWasm (run on gwasm-runner).

For illustration purposes, every step is executed by a separate command:

$ target/release/gudot generate
Writing input.json

$ target/release/gudot encrypt
Reading input.json
Writing enc_input.json
Writing keys.json

$ gwasm-runner target/release/gudot.wasm -- --subtasks=N
work dir: .../split
Reading enc_input.json
Writing enc_output.json

$ target/release/gudot decrypt
Reading keys.json
Reading enc_output.json
Writing output.json

$ target/release/gudot regress
Reading output.json
Reading input.json
Fitted model: y = -2.6898656093724984x + 93773.13902413758
Writing regress.json

$ target/release/gudot plot
Reading input.json
Reading regress.json
Writing plot.png

Running on Golem network

$ gwasm-runner --backend=Brass target/release/gudot.wasm -- --subtasks=N

When running on the Golem network, you may want to adjust some parameters specific to Brass Golem (e.g. the path to your Golem's data directory). To do so, you'll need to create a configuration file for the runner. You can find more information on this in the gwasm-runner README.

Components

  • generate --- creates input.json containing sample data in the format [[x1,...,xn][y1,...,yn]] (this step is optional - you can provide your own input.json);
  • encrypt --- generates a secret encryption key (keys.json), and encrypts input.json yielding enc_input.json;
  • gudot.wasm --- the main gWasm binary, meant to be run with gwasm-runner; it splits the encrypted data; sends parts to providers and combines partial results returned by the providers
  • decrypt --- decrypts the received results, producing output.json (reads keys.json and enc_output.json);
  • regress --- calculates parameters (slope, intercept) of the fitted linear model, producing regress.json
  • plot --- plots the regression line along with the input points, producing plot.png.
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