Trusted Data Sharing Model Integrating Blockchain and Deep Learning

Abstract

Privacy protection, establishment of trust, and quality assurance are known as very vital issues faced by the data sharing systems nowadays. This paper introduces a new framework that addresses these core limitations to integrate distributed ledger technology and machine learning practices. The suggested system will have a consortium blockchain design with incorporated neural network modules to provide automatic data validation and anomaly-detecting features. Smart contractbased governance leads to the safety of sensitive information due to the protection laid by multi-layered encryption protocols and transparency in operations. Evaluation of performance makes use of three different domains namely: their medical information systems, financial transaction networks and sensor data networks. The security is enhanced by $23.5 \%$, quality assessment is more accurate by $\mathbf{9 4. 2 \%}$, and the sustained processing capacity values 2,847 transactions per second according to the comparative assessment. The model provides the basis of cross-organizational cooperation with data as well as regulation and operation efficiency needs in distributed computing environments.