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Resurrecting Address Clustering in Bitcoin

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Financial Cryptography and Data Security (FC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13411))

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Abstract

Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.

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Notes

  1. 1.

    Our definition is unrelated to the isStandard test in the Bitcoin reference implementation that checks whether a transaction uses common script types.

  2. 2.

    https://github.com/maltemoeser/address-clustering-data.

  3. 3.

    This corresponds to a false positive rate of 0.044% for RF-1. We use a threshold of 0.997 for RF-2 to match the FPR.

  4. 4.

    https://arxiv.org/abs/2107.05749.

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Acknowledgement

We thank Rainer Böhme and Kevin Lee for their feedback on an earlier draft of this paper. This work is supported by NSF Award CNS-1651938 and a grant from the Ripple University Blockchain Research Initiative.

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Authors and Affiliations

  1. Princeton University, Princeton, USA

    Malte Möser & Arvind Narayanan

Authors
  1. Malte Möser
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  2. Arvind Narayanan
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Correspondence to Malte Möser .

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Editors and Affiliations

  1. Technion - Israel Institute of Technology, Haifa, Israel

    Ittay Eyal

  2. Texas A&M University, College Station, TX, USA

    Juan Garay

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Möser, M., Narayanan, A. (2022). Resurrecting Address Clustering in Bitcoin. In: Eyal, I., Garay, J. (eds) Financial Cryptography and Data Security. FC 2022. Lecture Notes in Computer Science, vol 13411. Springer, Cham. https://doi.org/10.1007/978-3-031-18283-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-18283-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18282-2

  • Online ISBN: 978-3-031-18283-9

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