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SoK: A Survey of Mixing Techniques and Mixers for Cryptocurrencies
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Blockchain technologies have overturned the digital finance industry by introducing a decentralized pseudonymous means of monetary transfer. The pseudonymous nature introduced privacy concerns, enabling various deanonymization techniques, which in turn spurred development of stronger anonymity-preserving measures. The purpose of this paper is to create a comprehensive survey of mixing techniques and implementations within the vast ecosystem surrounding anonymization tools and mechanisms available in blockchain cryptocurrencies. First, we begin by reviewing classifications used in the field. Then, we survey various obfuscation techniques, helping to delve into actual implementations and combinations of these techniques. Next, we identify the positive and negative attributes of the approaches and implementations included. Moreover, we examine the implications of anonymization tools for user privacy, including their effectiveness in preserving anonymity and susceptibility to attacks and vulnerabilities. Finally, we discuss the challenges and innovations for extending mixing services into the realm of smart contracts or cross-chain space.
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Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
A GCN-GRU hybrid achieved 0.9807 AUC-ROC on Bitcoin mixing transaction detection, reportedly outperforming all baselines, though evidence lacks error bars and the graph is a feature-similarity graph rather than the tr...
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