ZERO-KNOWLEDGE PROOFS FOR PRIVACY-PRESERVING AI AUTHENTICATION

Abstract

As machine learning spreads into fields of use that demand secure and private authentication, ensuring such authentication is becoming increasingly critical. Zero Knowledge Proofs (ZKPs) have been presented as a cryptographic technique of transforming authentication without data leakage [1]. In this research, the use of ZKPs in the AI authentication frameworks is looking into privacy, security and scalability. The model predictions are verified by the proposed system using advanced ZKP protocols like zkSNARKs and zkSTARKs without revealing model parameters or user inputs [3]. Our system is able to reach better computational efficiency and lower computation overhead through incorporation of Mystique conversion protocols [7] and fast ZK inference protocols such as ezDPS [6]. Results of experiments [5] show that frameworks with ZKP integrated authentication perform better than the standard encryption with respect to both security and performance in decentralized machine learning regimes. Moreover, the solution facilitates verifiability in Federated Learning by integrating blockchain, which helps to increase transparency and trust [4]. To overcome the data leakage issue, ZKPs are explored for use in decentralized AI frameworks where secure model deployment is required to generate personalized advice [4]. As this research shows, ZKPs offer transformative properties which can be used for authentication in AI systems – such as in healthcare, finance or IoT network – and thus increase the trust in AI driven solutions.