Verifiable evaluations of machine learning models using zkSNARKs
Tobin South Alexander Camuto Shrey Jain Shayla Nguyen Robert Mahari Christian Paquin Jason Morton Alex 'Sandy' Pentland
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.