This repository introduces a method for verifiable evaluation of machine learning models using zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs) via ezkl toolkit. The approach enables transparent evaluation of closed-source ML models, providing computational proofs of a model's performance metrics without exposing its internal weights. This technique ensures evaluation integrity and promotes trust in ML applications, applicable to any standard neural network model across various real-world examples.
The paper to accompany this repo can be found on arxiv.
In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results---whether over task accuracy, bias evaluations, or safety checks---are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can be packaged into verifiable evaluation attestations showing that models with fixed private weights achieve stated performance or fairness metrics over public inputs. These verifiable attestations can be performed on any standard neural network model with varying compute requirements. For the first time, we demonstrate this across a sample of real-world models and highlight key challenges and design solutions. This presents a new transparency paradigm in the verifiable evaluation of private models.
- For any of this t work you'll need to install and use the ezkl CLI and python package. Details of how to install can be found at
https://github.com/zkonduit/ezkl.git. The CLI or Python interfaces may be out of date in the latest versions, but the proof speed will be improved. - Simple model examples can be found in
/src. They run using the CLI and pytorch. - Experiments to replicate the paper results can be found in
/src/experiments. - Choose a new model or dataset to run and follow the example process to get an onnx version of the model and generate inference proofs on it.
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If you use this work or the ideas in it, please cite:
@misc{south2024verifiableevals,
title={Verifiable evaluations of machine learning models using zkSNARKs},
author={Tobin South and Alexander Camuto and Shrey Jain and Shayla Nguyen and Robert Mahari and Christian Paquin and Jason Morton and Alex 'Sandy' Pentland},
year={2024},
eprint={2402.02675}
}