SoK: The Role of Zero-Knowledge Proofs in Building Confidential and Trustworthy AI

Abstract

The verifiability of machine learning models and the privacy of training data have become critical concerns due to their widespread deployment in sensitive applications. Ensuring that a model performs as claimed, without revealing private data or algorithms, is a significant challenge. Zero-Knowledge Proof systems (ZKPs) have emerged as a promising cryptographic solution, enabling the verification of statements without disclosing underlying information. Their integration with blockchain technology further enhances trust and decentralization, offering robust solutions for secure and transparent AI systems. This paper explores the use of ZKPs in machine learning, focusing on privacy-preservation techniques, model verifiability, and confidential AI. It compares the differences and challenges of employing ZKPs in machine learning versus blockchains, highlighting their unique requirements and overlapping benefits. We review the basic concepts of ZKPs, advances such as zkSNARKs and zk-STARKs, and their applications in blockchainbased AI frameworks to ensure data integrity, immutability, and scalability. Furthermore, the paper delves into the practical implications of using ZKPs in AI, providing case studies and analyzing their scalability, performance, and limitations. We conclude by identifying key challenges and presenting future research directions to extend the applicability of ZKPs in AI, particularly in federated learning, model fairness, and decentralized AI pipelines.