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GuideEnricher: Protecting the Anonymity of Ethereum Mixing Service Users with Deep Reinforcement Learning

USENIX via YouTube

Overview

Watch a conference presentation from USENIX Security '24 exploring how deep reinforcement learning can enhance privacy guidelines for cryptocurrency mixing services. Learn about GuideEnricher, an innovative approach that proactively discovers potential anonymity vulnerabilities in Ethereum mixing services before deployment. Understand how the system uses a specialized DRL agent to automatically explore token transfer patterns and identify previously unknown ways that user privacy could be compromised. Examine the technical implementation details, including the customized task design and rule-based detection system that allows the agent to uncover new anonymity-compromising patterns while evading known ones. See how clustering methods and manual inspection are employed to analyze the agent's actions and continuously update privacy guidelines. Follow along as researchers from UC Santa Barbara demonstrate GuideEnricher's effectiveness across multiple mixing services and its ability to iteratively improve privacy protection through ongoing updates to both the detection system and DRL agents.

Syllabus

USENIX Security '24 - GuideEnricher: Protecting the Anonymity of Ethereum Mixing Service Users...

Taught by

USENIX

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