ENHANCING BLOCKCHAIN ANONYMITY USING SECURE MACHINE LEARNING APPROACH WITH ONION AND GARLIC ROUTING IN HEALTHCARE

The research explores an innovative approach to enhancing blockchain anonymity by integrating onion and garlic routing mechanisms with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. The study addresses growing concerns about privacy and traceability in blockchain transactions by developing a hybrid system that combines the encryption strengths of onion and garlic routing with the predictive capabilities of recurrent neural networks. Our experimental results demonstrate that this integration significantly enhances transaction privacy while maintaining optimal system performance. The proposed model achieved a 94.7% success rate in obscuring transaction origins and destinations, with a 37% improvement in routing efficiency compared to conventional methods. This work provides a promising framework for privacy-focused blockchain applications in secure communication systems and healthcare.