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

Summary

The authors present a verifiable secure inference scheme that uses a blockchain to verify the origins of both user inputs and model weights. Neural networks are converted into zero-knowledge proof constraints with optimized structures, and the scheme is applied to a regulatable privacy-preserving transaction system where anomaly detection runs on encrypted ledger data. The paper claims rigorous security proofs and reports computational and communication performance to demonstrate scalability.