Blockchain-Powered Framework for Trustworthy AI: Ensuring End-to-End Data and Model Integrity with Privacy Preservation

Abstract

Rapid deployment of AI, particularly in sectors such as healthcare, finance and smart infrastructure, necessitates reliable capabilities (1) to assure data integrity (2) model update verifiability and (3) privacy preservation along the AI lifecycles. In this paper, we propose a blockchain based framework for trustful AI and traceable end-to-end training and inference. The framework leverages a distributed ledger to immutably bind the hash-locked commitment of data and model parameters through deployed smart contracts defining automated verification protocols. Secure multiparty computation [PADDP13] and zero-knowledge proofs are some of the privacy mechanisms used during collaborative training in federated learning environments to keep sensitive data safe. In expanding on our work in blockchain-based AI integrity frameworks, the new architecture shown integrates anomaly detection and provenance tracking to prevent malicious contributions. Extensive experimental evaluations over standard datasets confirm the ability of our solution to efficiently provide reliable verification without additional computational background for domain independence. This work has the potential to form the bedrock of future AI deployments that are transparent, safe, and ethically responsible.