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

Simulated Blockchains for Machine Learning Traceability and Transaction Values in the Monero Network