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The development of clustering heuristics has demonstrated that Bitcoin is notcompletely anonymous. Currently, existing clustering heuristics only considerconfirmed transactions recorded in the Bitcoin blockchain. However, unconfirmedtransactions in the mempool have yet to be utilized to improve the performanceof the clustering heuristics.  In this paper, we bridge this gap by combining unconfirmed and confirmedtransactions for clustering Bitcoin addresses effectively. First, we present adata collection system for capturing unconfirmed transactions. Two case studiesare performed to show the presence of user behaviors in unconfirmedtransactions not present in confirmed transactions. Next, we apply thestate-of-the-art clustering heuristics to unconfirmed transactions, and theclustering results can reduce the number of entities after applying, forexample, the co-spend heuristics in confirmed transactions by 2.3%. Finally, wepropose three novel clustering heuristics to capture specific behavior patternsin unconfirmed transactions, which further reduce the number of entities afterthe application of the co-spend heuristics by 9.8%. Our results demonstrate theutility of unconfirmed transactions in address clustering and further shedlight on the limitations of anonymity in cryptocurrencies. To the best of ourknowledge, this paper is the first to apply the unconfirmed transactions inBitcoin to cluster addresses.

Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering