Towards Understanding and Analyzing Instant Cryptocurrency Exchanges

In this paper, we examine a novel category of services in the blockchain ecosystem termed Instant Cryptocurrency Exchange (ICE) services. Originally conceived to facilitate cross-chain asset transfers, ICE services have, unfortunately, been abused for money laundering activities due to two key features: the absence of a strict Know Your Customer (KYC) policy and incomplete on-chain data of user requests. As centralized and non-transparent services, ICE services pose considerable challenges in the tracing of illicit fund flows laundered through them. Our comprehensive study of ICE services begins with an analysis of their features and workflow. We classify ICE services into two distinct types: Standalone and Delegated. We then perform a measurement analysis of ICE services, paying particular attention to their usage in illicit activities. Our findings indicate that a total of 12,473,290 illegal funds have been laundered through ICE services, and 432 malicious addresses were initially funded by ICE services. Based on the insights from measurement analysis, we propose a matching algorithm designed to evaluate the effectiveness of ICE services in terms of efficiency and prevention of traceability. Our evaluation reveals that 92% of the user requests analyzed were completed in less than three minutes, underscoring the efficiency of ICE services. In addition, we demonstrate that the algorithm is effective in tracing illicit funds in situations where ICE services are used in malicious activities. To engage the community, the entire dataset used in this study is open-source.