Simulated Blockchains for Machine Learning Traceability and Transaction Values in the Monero Network
This work addresses privacy vulnerabilities in cryptocurrency systems for users and regulators, but it is incremental as it builds on existing simulation and machine learning approaches without fully breaking Monero's core privacy claims.
The researchers tackled the problem of tracing transactions and identifying participants in the privacy-focused Monero blockchain by developing simulated blockchains with ground truth data and applying machine learning to featurized transactions. They achieved partial success in identifying individuals and groups on simulated data and applied the method to real data to identify ShapeShift exchange transactions, but did not reveal hidden transaction values.
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.