Blockchain-Enhanced Verifiable Secure Inference for Regulatable Privacy-Preserving Transactions

Abstract

In the field of artificial intelligence, secure model inference is essential for protecting data confidentiality, which allows users to interact with trained models for decision-making support without privacy leakage. However, current secure inference methods often overlook the simultaneous verification of data origins for both user inputs and model weights, which is crucial for maintaining the integrity of inference outcomes. In this study, we present a novel verifiable secure inference scheme that leverages blockchain to enhance the verifiability of both the inference process and the origins of user inputs and model weights. We integrate the decentralized ledger to store the committed inputs and weights, serving as convincing data origins. We then transform neural networks into zero-knowledge proof constraints with optimized structures for the inference process. To illustrate its application scenario, we propose a regulatable privacy-preserving transaction scheme. Its regulation depends on anomaly detection on private transactions without privacy leakage, which takes the encrypted ledger as the data source and the committed detection model as the parameter source to perform our verifiable secure inference. We provide rigorous security proofs for our schemes, demonstrating their authenticity and privacy. We implement them to demonstrate their scalability through analyzing their computational and communication performance.