CRSep 29, 2020
Tracking Mixed BitcoinsTin Tironsakkul, Manuel Maarek, Andrea Eross et al.
Mixer services purportedly remove all connections between the input (deposited) Bitcoins and the output (withdrawn) mixed Bitcoins, seemingly rendering taint analysis tracking ineffectual. In this paper, we introduce and explore a novel tracking strategy, called \emph{Address Taint Analysis}, that adapts from existing transaction-based taint analysis techniques for tracking Bitcoins that have passed through a mixer service. We also investigate the potential of combining address taint analysis with address clustering and backward tainting. We further introduce a set of filtering criteria that reduce the number of false-positive results based on the characteristics of withdrawn transactions and evaluate our solution with verifiable mixing transactions of nine mixer services from previous reverse-engineering studies. Our finding shows that it is possible to track the mixed Bitcoins from the deposited Bitcoins using address taint analysis and the number of potential transaction outputs can be significantly reduced with the filtering criteria.
CRJun 13, 2019
Probing the Mystery of Cryptocurrency Theft: An Investigation into Methods for Taint AnalysisTin Tironsakkul, Manuel Maarek, Andrea Eross et al.
Since the creation of Bitcoin, transaction tracking is one of the prominent means for following the movement of Bitcoins involved in illegal activities. Although every Bitcoin transaction is recorded in the blockchain database, which is transparent for anyone to observe and analyse, Bitcoin's pseudonymity system and transaction obscuring techniques still allow criminals to disguise their transaction trail. While there have been a few attempts to develop tracking methods, there is no accepted evaluation method to measure their accuracy. Therefore, this paper investigates strategies for transaction tracking by introducing two new tainting methods, and proposes an address profiling approach with a metrics-based evaluation framework. We use our approach and framework to compare the accuracy of our new tainting methods with the previous tainting techniques, using data from two real Bitcoin theft transactions and several related control transactions.