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.
A more mature system was BitConeView, presented by Battista and Donato in 2015 @cite_4 . This was among the first to provide a sensible GUI to inspect how a particular UTXO propagated through the network. In order to explain what it means for money to move, the authors came up with purity' -- basically a version of haircut tainting. They only evaluated the usability of their system informally, and came to the conclusion that more improvements were necessary to the way purity was presented to the user.
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Bitcoin is a digital currency whose transactions are stored into a public ledger, called blockchain, that can be viewed as a directed graph with more than 70 million nodes, where each node represents a transaction and each edge represents Bitcoins flowing from one transaction to another one. We describe a system for the visual analysis of how and when a flow of Bitcoins mixes with other flows in the transaction graph. Such a system relies on high-level metaphors for the representation of the graph and the size and characteristics of transactions, allowing for high level analysis of big portions of it.