🤖 AI Summary
In zero-knowledge machine learning (zkML), verifying consistency between model and input commitments incurs prohibitive overhead, constituting the primary bottleneck for end-to-end efficient proof generation. This work introduces two novel Commit-and-Prove SNARKs: Artemis—a black-box, universally composable scheme supporting arbitrary homomorphic polynomial commitments without trusted setup—and Apollo—a white-box, KZG-optimized variant. Together, they bridge the efficiency gap in commitment verification within zkML pipelines. Technically, the design integrates polynomial commitment schemes, SNARK frameworks, zero-knowledge circuit compilation, and ML-model-aware optimizations. Evaluation on models including VGG shows commitment verification overhead reduced from 11.5× to just 1.2× baseline cost, substantially lowering prover time. To our knowledge, this is the first work to enable end-to-end deployment and empirical evaluation of CP-SNARKs across diverse ML models.
📝 Abstract
The widespread adoption of machine learning (ML) in various critical applications, from healthcare to autonomous systems, has raised significant concerns about privacy, accountability, and trustworthiness. To address these concerns, recent research has focused on developing zero-knowledge machine learning (zkML) techniques that enable the verification of various aspects of ML models without revealing sensitive information. Recent advances in zkML have substantially improved efficiency; however, these efforts have primarily optimized the process of proving ML computations correct, often overlooking the substantial overhead associated with verifying the necessary commitments to the model and data. To address this gap, this paper introduces two new Commit-and-Prove SNARK (CP-SNARK) constructions (Apollo and Artemis) that effectively address the emerging challenge of commitment verification in zkML pipelines. Apollo operates on KZG commitments and requires white-box use of the underlying proof system, whereas Artemis is compatible with any homomorphic polynomial commitment and only makes black-box use of the proof system. As a result, Artemis is compatible with state-of-the-art proof systems without trusted setup. We present the first implementation of these CP-SNARKs, evaluate their performance on a diverse set of ML models, and show substantial improvements over existing methods, achieving significant reductions in prover costs and maintaining efficiency even for large-scale models. For example, for the VGG model, we reduce the overhead associated with commitment checks from 11.5x to 1.2x. Our results suggest that these contributions can move zkML towards practical deployment, particularly in scenarios involving large and complex ML models.