BitIodine: Extracting Intelligence from the Bitcoin Network

Anonymity in Bitcoin, a peer-to-peer, decentralized electronic currency system, is a complicated issue. While the Bitcoin technology can support strong anonymity, the current implementation is not strongly anonymous. In this thesis we present a tool, BitIodine, for extracting intelligence from the Bitcoin network, by grouping transaction graphs into user graphs, thanks to heuristics that set ownership relations between addresses and users, and profiling activity at different abstraction levels: the transaction level, the address level, and up to the user level. BitIodine makes use of a new approach for creating the user graph, and is actually a collection of deployable modules to parse the blockchain, cluster addresses, classify addresses and users, graph, export and visualize elaborated information from the Bitcoin network. In particular, we show that using a combination of modules it is possible to prove that one bitcoin address actually belongs to the Silk Road, the large black market.