What question did this study set out to answer?

The aim is to enhance privacy and anonymity in blockchain transactions using machine learning techniques.

January 6, 20260 citationsOpen Access

Enhancing Blockchain Anonymity Using Secure Machine Learning Approach With Onion and Garlic Routing in Healthcare

JTJournal of Theoretical and Applied Information Technology

Key Points

The aim is to enhance privacy and anonymity in blockchain transactions using machine learning techniques.
Developed a hybrid system combining onion and garlic routing with recurrent neural networks.
Implemented Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for deep learning.
Conducted experimental assessments to measure transaction privacy and routing efficiency.
Achieved a 94.7% success rate in obscuring transaction origins and destinations.
Demonstrated a 37% improvement in routing efficiency compared to traditional methods.

Abstract

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.

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