Clustering bitcoin accounts using heuristics

In this O'Reilly Data Show Podcast: Sarah Meiklejohn on analytic applications for blockchain and cryptocurrency technology.

Editor’s note: we’ll explore present and future applications of cryptocurrency and blockchain technologies at our upcoming Radar Summit: Bitcoin & the Blockchain on Jan. 27, 2015, in San Francisco.

A few data scientists are starting to play around with cryptocurrency data, and as bitcoin and related technologies start gaining traction, I expect more to wade in. As the space matures, there will be many interesting applications based on analytics over the transaction data produced by these technologies. The blockchain — the distributed ledger that contains all bitcoin transactions — is publicly available, and the underlying data set is of modest size. Data scientists can work with this data once it’s loaded into familiar data structures, but producing insights requires some domain knowledge and expertise.

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I recently spoke with Sarah Meiklejohn, a lecturer at UCL, and an expert on computer security and cryptocurrencies. She was part of an academic research team that studied pseudo-anonymity (“pseudonymity”) in bitcoin. In particular, they used transaction data to compare “potential” anonymity to the “actual” anonymity achieved by users. A bitcoin user can use many different public keys, but careful research led to a few heuristics that allowed them to cluster addresses belonging to the same user:

“In theory, a user can go by many different pseudonyms. If that user is careful and keeps the activity of those different pseudonyms separate, completely distinct from one another, then they can really maintain a level of, maybe not anonymity, but again, cryptographically it’s called pseudo-anonymity. So, if they are a legitimate businessman on the one hand, they can use a certain set of pseudonyms for that activity, and then if they are dealing drugs on Silk Road, they might use a completely different set of pseudonyms for that, and you wouldn’t be able to tell that that’s the same user.

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