Scalable Zero-knowledge Proofs for Non-linear Functions in Machine Learning

Source code and the implementation of the USENIX Security’24 paper - Scalable Zero-knowledge Proofs for Non-linear Functions in Machine Learning.

Introduction

A novel scalable ZK proof framework for non-linear mathematical functions in machine learning based on LUT techniques.

Contents

This repository consists of the following parts:

• src: Implementations for our ZKP protocols.
• test: Test script for performance evaluation.

Installation

NOTE: Our implementation is built based on emp-zk and a complete compilation process is required. For any issues occurred during step 1, please refer to emp-zk troubleshooting.

1. Install emp-tool, emp-ot and emp-zk.

python install.py --deps --tool --ot --zk

2. Update code for zkMath.

cp -rf src/* emp-zk/emp-zk/ && rm -rf src \
&& cp -rf test/* emp-zk/test/ && rm -rf test \
&& mv -f CMakeLists.txt emp-zk/

3. Recompile the code, then the binary executables can be found at ./bin

cd emp-zk
cmake .
make -j8
sudo make install

Usage

Testing on localhost

./run ./bin/[binary]

Testing on two

1. Change the IP address in the test code, or use tmux command to initialize another terminal

2. run ./bin/[binary] 1 [port] on one machine and

   run ./bin/[binary] 2 [port] on the other machine.

Disclaimer

This repository is a proof-of-concept prototype.