Sandia National Laboratories, a federally funded research centre run by Lockheed Martin, has revealed that it has created a set of requirements for an analysis tool that can be used to overcome the challenges brought by bitcoin and to assist law enforcement.

In an online post, Sandia said that bitcoin is increasingly being used by criminals, making it harder for law enforcement to keep track of users. Andrew Cox is leading a team at Sandia that is helping law enforcement get a better handle on tracking illicit digital currency transactions.

“[I]t has been clear that criminals have been pioneers in using Bitcoin. They use it for drugs, for guns, child pornography, and all sorts of terrible stuff”, noted Cox.

Sandia is developing the analysis tool for the Department of Homeland Security (DHS) Science and Technology (S&T) directorate, however, it could be delivered to other federal law enforcement agencies as well. DHS S&T requested Sandia to set up a graphical user interface or a front end on the research environment so agents can test the algorithms Sandia is using in actual investigations.

One of the key challenges faced by law enforcement includes the significant time and resources needed to pinpoint bitcoin users. Cox explains that there is not a “silver bullet” algorithm to effectively de-anonymize bitcoin, adding that doing so would involve cross-referencing anonymous data with other, traditional sources of investigative data to identify suspects.

“To be successful, the reality is it’s going to take different types of algorithms and additional types of investigative techniques including good old-fashioned police work,” he says. “They’re all going to have to be combined.”

Sandia conducted a systems analysis of illicit e-commerce focusing on Bitcoin. The team set up a research environment to experiment with other algorithms that can de-anonymize illicit bitcoin users. Once de-anonymization occurs, law enforcement can link the Bitcoin addresses to a specific alias and hence know all of the bitcoin addresses they need to deal with.

During the analysis, the researchers understood that the same users are using the different bitcoin addresses. They are now in the process of generating their own methods by characterizing transactions of bitcoin users and applying machine learning methods to uncover patterns of interest. Sandia will continue to work on the algorithmic research and focus on developing a graphical user interface.

“In many ways,” said Cox, “figuring out how to effectively combat illicit Bitcoin commerce and reduce its perception as a tool of criminals can encourage more people and companies to adopt Bitcoin for legitimate purposes.”