Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering

The advancement of clustering heuristics has demonstrated that the addresses of Bitcoin, which are protected by their anonymous mechanisms, can be de-anonymized. While the state-of-the-art (SOTA) clustering heuristics focus on confirmed transactions stored in the blockchain, they ignore unconfirmed transactions in the mempool. These unconfirmed transactions contain information about transactions before being stored in the blockchain, covering additional address associations that can improve Bitcoin address clustering. In this paper, we bridge the gap by combining confirmed and unconfirmed transactions for effective Bitcoin address clustering. First, we introduce a reliable data collection framework to collect both confirmed and unconfirmed Bitcoin transactions. Second, we propose two novel clustering heuristics that exploit specific behavior patterns in unconfirmed transactions and uncover additional address associations. Finally, we construct a labeled dataset and experimentally show that the effectiveness of our proposed clustering heuristics, improving recall by at least three times with higher precision compared to the SOTA clustering heuristics. Our findings show the value of unconfirmed transactions for Bitcoin address clustering and further reveal the challenges of achieving anonymity in Bitcoin. To the best of our knowledge, our study is the first to explore unconfirmed transactions for Bitcoin address clustering.