  Monero is a popular crypto-currency which focuses on privacy. The blockchain
uses cryptographic techniques to obscure transaction values as well as a `ring
confidential transaction' which seeks to hide a real transaction among a
variable number of spoofed transactions. We have developed training sets of
simulated blockchains of 10 and 50 agents, for which we have control over the
ground truth and keys, in order to test these claims. We featurize Monero
transactions by characterizing the local structure of the public-facing
blockchains and use labels obtained from the simulations to perform machine
learning. Machine Learning of our features on the simulated blockchain shows
that the technique can be used to aide in identifying individuals and groups,
although it did not successfully reveal the hidden transaction values. We apply
the technique on the real Monero blockchain to identify ShapeShift
transactions, a cryptocurrency exchange that has leaked information through
their API providing labels for themselves and their users.
