MIT Technology Review Says New Taintchain Algorithm Looks to Track Stolen Bitcoins Soon
There is a paradox surrounding cryptocurrencies. Cryptos inalterable evidence, stored on thousands of computers, of every Bitcoin transaction that's ever taken place. Many of the transactions recorded on that distributed ledger are crimes: Billions of dollars in stolen funds, contraband deals, and paid ransoms sitting in plain sight, yet obscured by unidentifiable Bitcoin addresses and, in many cases, tangles of money laundering.
Suppose you are foolish enough to let people know that you have a lot of Bitcoin with you and if an intruder forces the keys out of you, how do you go about tracing the stolen loop?
Professor Ross Anderson of the Computer Laboratory, University of Cambridge sums up the approach perfectly in this video.
In 2018, a group of Cambridge cybersecurity researchers published a paper that argues for a new way of tracing “tainted” coins in the blockchain, particularly ones that have been stolen or extorted from victims and then sent through a series of transactions to hide their ill-gotten origin. Rather than try to offer any new detective tricks to identify the source of a Bitcoin transaction hiding behind a pseudonymous address, their idea instead redefines what constitutes a dirty bitcoin.
Based on a legal precedent from an 1816 British court decision, they posit that the first coin that leaves a Bitcoin address should be considered the same coin as the first one that went into it, carrying with it all of that coin's criminal history. And if that coin was once stolen from someone, he or she may be allowed to claim it back even after it has passed through multiple addresses.
The systems are known as “haircut” and “poison”. For example, if three stolen coins are mixed with seven coins, according to the first method, 30% of the resulting coins would be dirty; and according to the second, then 10 coins would be dirty. Following these two systems, then half of the transactions with bitcoins would have to be considered as dirty.