A group of students and staff from Trinity College Dublin, which is developing a tradition of research into crypto projects, is building a bitcoin ‘credit check’ database to make the digital currency more transparent.
The team, led by Professor Donal O’Mahony, is hoping that the database will enable potential business partners to identify possible indicators for fraudulent business practices or money laundering, whilst still granting sufficient anonymity.
“We have been watching the progress of bitcoin and have been fascinated by the market uptake, the uses people have put it to, but we’ve also been struck by how little is known about what is actually going on at a transaction level.”
Building a big picture
“Even though the [bitcoin] system is designed not to be regulated,” said O’Mahony, “it would give people some comfort if there was a way to build a big picture of what was going on. Identifying fraudsters and helping people to avoid them would also be a useful thing.”
To that end, the team is trying to group bitcoin addresses together into clusters, by correlating the addresses used to make payments with those that were used to receive change. This knowledge is then combined into a database of bitcoin addresses, to enable the team to link an address to a pocket of fraudulent activity.
The bitcoin database is currently working in a lab setting, but O’Mahony says that they are “continuing to break new ground,” adding that he would “not be surprised if one or more of [the students] saw a business opportunity”.
Like the Dublin work, BitIodine “parses the blockchain, clusters addresses that are likely to belong to a same user or group of users, classifies and labels them and finally visualises complex information extracted from the bitcoin network”.
He pointed out that “the main difference between my work and that currently being carried out in Dublin is that I do not mean to evaluate good and bad actors in the bitcoin network”. Instead he hopes BitIodine will become “the skeleton for building more complex frameworks for bitcoin forensic analysis”.