Efficient Zero-Knowledge Proofs for Typical Non-Linear Functions in Machine Learning

Abstract

Zero-knowledge proofs (ZKPs) have been used to protect the integrity of machine learning (ML) models. However, existing ZKPs for ML are still inefficient, mainly due to the computational cost of evaluating non-linear functions. In this paper, we propose a ZKP framework for typical non-linear functions in ML, including Sigmoid, Softmax, etc. Compared to the state-of-the-art Hao et al. (USENIX Security ’24), our protocols obtain 115.6-2384.4× and 296.8-4104.7× runtime improvements for prover and verifier, respectively, with a 37.91269.5× reduction in proof size.