The rapid expansion of cryptocurrencies and the development of crypto infrastructure, as well as vulnerabilities such as cryptocurrency mixers, have been a source of concern for government agencies responsible for financial security.
Many people use cryptocurrency mixers to keep cryptocurrency transactions private by mixing potentially identifiable cryptocurrency funds with lots of other funds. These services are often used to anonymously transfer funds between different services and do not require Know Your Customer (KYC) checks.
Therefore, the risk of using a cryptocurrency mixer to launder money or hide gains is considerable. Mixers and online gambling sites are often involved in the worst money laundering problems because they tend to handle the vast majority of dirty money. For example, illicit bitcoin (BTC) processed annually by mixers accounts for about a quarter of all inflows to bitcoin, while the proportion of money laundered through exchanges and gambling has remained relatively stable (66% to 72%).
There are two types of Bitcoin mixers: centralized mixers and decentralized mixers. Companies that accept bitcoins and give back different bitcoins are called centralized mixers and offer an easy solution for mixing bitcoins.
Decentralized mixers use protocols like CoinJoin to obfuscate transactions using fully coordinated or peer-to-peer (P2P) methods. Essentially, the protocol allows a large group of users to aggregate a certain amount of bitcoin, which is then redistributed so that everyone receives a certain amount of bitcoin. However, no one knows who received what, or where it came from.
Other types of mixers include obfuscation-based and zero-knowledge-based mixers. Obfuscation-based mixers, commonly referred to as decoy-based mixers, employ various methods to hide the user’s transaction graph. On the other hand, an adversary with sufficient resources can recreate the transaction graph using various methods.
Instead, zero-knowledge-based mixers rely heavily on advanced cryptographic techniques like zero-knowledge proofs to completely remove the transaction graph. The most notable disadvantage of this strategy is that it requires a lot of encryption, which can limit scalability.