On-chain zero-knowledge machine learning: An overview and comparison

Zero-knowledge proofs introduce a mechanism to prove that certain computations were performed without revealing any underlying information and are used commonly in blockchain-based decentralized apps (dapps). This cryptographic technique addresses trust issues prevalent in blockchain applications, and has now been adapted for machine learning (ML) services, known as Zero-Knowledge Machine Learning (ZKML). By leveraging the distributed nature of blockchains, this approach enhances the trustworthiness of ML deployments, and opens up new possibilities for privacy-preserving and robust ML applications within dapps. This paper provides a comprehensive overview of the ZKML process and its critical components for verifying ML services on-chain. Furthermore, this paper explores how blockchain technology and smart contracts can offer verifiable, trustless proof that a specific ML model has been used correctly to perform inference, all without relying on a single trusted entity. Additionally, the paper compares and reviews existing frameworks for implementing ZKML in dapps, serving as a reference point for researchers interested in this emerging field. • An analytical and synthetic review of core on-chain ZKML concepts, supported by an extensive examination of both white and grey literature, establishing a foundational understanding of the field. • Through a detailed analysis, modelling, and descriptive approaches, the paper outlines the processes integral to on-chain ZKML. The study is focused on two distinct frameworks – EZKL and Orion , highlighting the differences between the two approaches, as well as the difference between the underlying ZKP systems, where the former framework is based on zk-SNARKs and the latter on zk-STARKs. • A laboratory experiment, coupled with a comparative analysis and use case execution comparison, was conducted to implement basic neural networks (NNs) across the two chosen frameworks, highlighting their capabilities and limitations in supporting on-chain ZKML.