Computational model to enhance the admissibility of Bitcoin tracing heuristics at the Court of Law
PI: Prof. K.P. Chow, HKU
The cryptocurrency Bitcoin was created in 2008 and has received lots of attention recently. The current price of 1 Bitcoin is over US$ 35,000. Bitcoin is based on strong cryptography, which enable users to trade Bitcoin anonymously. The anonymous nature of Bitcoin makes it particularly suitable for crime, such as ransom for ransomware, money laundering by organized crime, etc. Recent development in deterministic wallet allows Bitcoin to be traded with new and unused Bitcoin addresses for every new transaction, which make it almost impossible to identify owners of Bitcoin addresses. Many Bitcoin address clustering algorithms were proposed in the past that have been successfully grouped “unrelated” Bitcoin addresses together and linked to suspected criminals. On the other hand, none of these clustering algorithms have been successfully admitted at the Court of Law because all of them are heuristic in nature. According to Daubert’s principle, to have an “algorithm” be admissible, it needs to have a known error rate. No clustering algorithm was able to report an error rate.
We propose here to build a computational model which can be used to validate the validity of the clustering algorithms and to measure the corresponding error rates. With these results, the clustering algorithms will have a higher chance be admitted at the Court of Law. The computational model will be a Bitcoin transaction simulator with built-in traces. The simulator will simulate the real-world Bitcoin transactions and collect statistics at the same time. As it is a simulator, we can always identify the actual owners behind the Bitcoin addresses, and therefore able to measure the error rate of the Bitcoin clustering algorithms.