Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain
This work addresses privacy issues for Bitcoin users by improving detection of CoinJoin transactions, but it is incremental as it builds on limited prior research in this area.
The research tackled the problem of detecting CoinJoin transactions on the Bitcoin blockchain to address privacy concerns, resulting in the development of refined heuristics by analyzing transactions up to block 760,000 from implementations like JoinMarket, Wasabi, and Whirlpool.
This research delves into the intricacies of Bitcoin, a decentralized peer-to-peer network, and its associated blockchain, which records all transactions since its inception. While this ensures integrity and transparency, the transparent nature of Bitcoin potentially compromises users' privacy rights. To address this concern, users have adopted CoinJoin, a method that amalgamates multiple transaction intents into a single, larger transaction to bolster transactional privacy. This process complicates individual transaction tracing and disrupts many established blockchain analysis heuristics. Despite its significance, limited research has been conducted on identifying CoinJoin transactions. Particularly noteworthy are varied CoinJoin implementations such as JoinMarket, Wasabi, and Whirlpool, each presenting distinct challenges due to their unique transaction structures. This study delves deeply into the open-source implementations of these protocols, aiming to develop refined heuristics for identifying their transactions on the blockchain. Our exhaustive analysis covers transactions up to block 760,000, offering a comprehensive insight into CoinJoin transactions and their implications for Bitcoin blockchain analysis.