Graph-Theoretical Approaches Integrated with Deep Learning for Data Privacy Enhancement in Blockchain Networks

Blockchain creates a decentralized platform which maintains strong security while showing all transactions completely transparently throughout diverse applications from financial networks to supply chain processes. Its major shortcoming is making private data visible because hackers can easily find private transactions from exposed user data. Present techniques work effectively with transaction analysis but do not provide effective privacy protection so user information remains in danger. To address this concern we develop a GCN-PPA hybrid model that brings together Graph Convolutional Networks and Private Proximity Awareness. GCN analyzes blockchain transaction data naturally by studying its built-in network design. By using PPA we ensure the privacy of sensitive data. Our method protects digital analytics results without reducing system efficiency for user privacy. Our proposed GCN-PPA model delivers 98.6% accurate pattern detection results on transactions while maintaining complete data protection which outperforms conventional privacy methods. Its exceptional results demonstrate how blockchain applications can perform safely across privacy-dependent fields while maintaining essential features.