The first six months of 2018 have seen cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has caused both researchers and law enforcement to look for ways to trace criminal proceeds. Although tracing algorithms have improved recently, they still yield an enormous amount of data of which very few datapoints are relevant or interesting to investigators, let alone ordinary bitcoin owners interested in provenance. In this work we describe efforts to visualize relevant data on a blockchain. To accomplish this we come up with a graphical model to represent the stolen coins and then implement this using a variety of visualization techniques.
Unlike BitConduit and similar systems, we are not doing any actor characterization in our visualisation tool @cite_9 . The generation of graph colours is exogenous, relying on external theft reports or of software that analyses patterns of mixes, ransomware and other undesirable activity.
@cite_9
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BitConduite is a system we are developing for the visual exploration of financial activity on the Bitcoin network. Bitcoin is the largest digital pseudo-currency worldwide and its study is of increasing interest and importance to economists, bankers, policymakers, and law enforcement authorities. All financial transactions in Bitcoin are available in an openly accessible online ledger—the (Bitcoin) blockchain. Yet, the open data does not lend itself easily to an analysis of how different individuals and institutions—or entities on the network—actually use Bitcoin. Our system BitConduite offers a data transformation back end that gives us an entity-based access to the blockchain data and a visualization front end that supports a novel high-level view on transactions over time. In particular, it facilitates the exploration of activity through filtering and clustering interactions. We are developing our system with experts in economics and will conduct a formal user study to assess our approach of Bitcoin activity analysis.