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

【Author】 Kersic, Vid; Karakatic, Saso; Turkanovic, Muhamed

【Source】JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES

【影响因子】8.839

【Abstract】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.

【Keywords】Zero-knowledge proofs; Machine learning; ZKML; Decentralized AI

【发表时间】2024 NOV

【收录时间】2024-10-21

【文献类型】综述

【主题类别】

区块链技术-核心技术-零知识证明