Zero-Knowledge Proofs for Verifiable AI Model Training

Abstract

With the proliferation of AI models in critical applications, ensuring their integrity and privacy during training has become paramount. Zero-knowledge proofs (ZKPs) offer a promising approach to verify the correctness of computations without revealing sensitive data. This paper explores the application of ZKPs in the context of AI model training, focusing on the verification of training processes while maintaining data confidentiality. We propose a framework where ZKPs are utilized to validate the execution of machine learning algorithms on private datasets, ensuring that the outcomes are correct and trustworthy without compromising data privacy. Through theoretical analysis and practical implementation examples, we demonstrate the feasibility and effectiveness of our approach in enhancing the transparency and security of AI model training.