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Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph

  • Conference paper
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Financial Cryptography and Data Security (FC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10323))

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Abstract

Bitcoin exchanges operate between digital and fiat currency networks, thus providing an opportunity to connect real-world identities to pseudonymous addresses, an important task for anti-money laundering efforts. We seek to characterize, understand, and identify patterns centered around exchanges in the context of a directed hypergraph model for Bitcoin transactions. We introduce the idea of motifs in directed hypergraphs, considering a particular 2-motif as a potential laundering pattern. We identify distinct statistical properties of exchange addresses related to the acquisition and spending of bitcoin. We then leverage this to build classification models to learn a set of discriminating features, and are able to predict if an address is owned by an exchange with \(>80\%\) accuracy using purely structural features of the graph. Applying this classifier to the 2-motif patterns reveals a preponderance of inter-exchange activity, while not necessarily significant laundering patterns.

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Notes

  1. 1.

    Terminologically, we can call these hypermotifs or hypergraph motifs, but for simplicity here we will just call them motifs.

  2. 2.

    We include addresses in exactly one intersection, ignoring addresses in only a tail or head of one of the transactions, and also addresses in more than two intersections.

  3. 3.

    Note the similarities of the counts for \(\alpha \) and \(\alpha '\), on the one hand, and \(L_1\) and \(L_2\), on the other, due to isomorphism with respect to the ordering of \(E_1\) and \(E_2\).

  4. 4.

    https://bitcoin.org/en/download.

  5. 5.

    https://www.walletexplorer.com, accessed January 16 2016.

  6. 6.

    Recall that this is at the address level, and each exchange has a set of addresses they own. The frequency of each exchange (e.g. BTC-e.com) in STBs is shown in Fig. 10 in the Appendix, and is highly correlated with the number of addresses each exchange has, see Fig. 11.

  7. 7.

    All experiments were run using Python 2.7 and the scikit-learn and numpy packages.

  8. 8.

    For an address a, siblings are addresses that have been a co-input or co-output, successors are addresses that have been an output when a was an input, and predecessors are addresses that were an input when a was an output.

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Acknowledgements

This material is based on work supported in part by the Department of Energy National Nuclear Security Administration under Award Number(s) DE-NA0002576. It is also supported in part under the Laboratory Directed Research and Development Program at the Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy.

Author information

Authors and Affiliations

  1. North Carolina State University, Raleigh, USA

    Stephen Ranshous & Nagiza F. Samatova

  2. Pacific Northwest National Laboratory, Seattle, WA, USA

    Cliff A. Joslyn, Sean Kreyling, Curtis L. West & Samuel Winters

  3. Oak Ridge National Laboratory, Oak Ridge, USA

    Nagiza F. Samatova

  4. Pacific Northwest National Laboratory, Richland, USA

    Kathleen Nowak

Authors
  1. Stephen Ranshous
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  2. Cliff A. Joslyn
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  3. Sean Kreyling
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  4. Kathleen Nowak
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  5. Nagiza F. Samatova
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  6. Curtis L. West
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  7. Samuel Winters
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Corresponding author

Correspondence to Stephen Ranshous .

Editor information

Editors and Affiliations

  1. Leibniz Universität Hannover, Hannover, Germany

    Michael Brenner

  2. New Jersey Institute of Technology, Newark, New Jersey, USA

    Kurt Rohloff

  3. New York University, New York, New York, USA

    Joseph Bonneau

  4. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Andrew Miller

  5. University of Luxembourg, Luxembourg, Luxembourg

    Peter Y.A. Ryan

  6. University of Melbourne, Parkville, Victoria, Australia

    Vanessa Teague

  7. University of Stirling, Stirling, United Kingdom

    Andrea Bracciali

  8. University of Trento, Trento, Italy

    Massimiliano Sala

  9. University of Trento, Trento, Italy

    Federico Pintore

  10. Agari Inc., San Mateo, California, USA

    Markus Jakobsson

Appendices

A Exchanges Used

Fig. 10.
figure 10

Frequency of exchanges in STBs.

Full size image
Fig. 11.
figure 11

Relationship between the number of vertices owned by each exchange and the frequency of that exchange in STBs.

Full size image

B Features Used

An address’ feature matrix is composed of the following features, extracted from each day of the data and then aggregated. Features prefixed with “*” are those removed in the second experiment, where exchange label based features are removed.

  1. 1.

    \(total\_bitcoin\_received\) – How much BTC the address received from transaction outputs over the full time window.

  2. 2.

    \(total\_bitcoin\_spent\) – How much BTC the address spent as transaction inputs over the full time window.

  3. 3.

    \(bitcoin\_balance\) – Total bitcoin received minus total bitcoin spent.

  4. 4.

    \(num\_predecessors\) – How many unique addresses have been an input to transactions where this address was an output.

  5. 5.

    \(num\_transaction\_outputs\) – How many times this address has been used in a transaction output.

  6. 6.

    \(num\_successors\) – How many unique addresses have been an output in transactions where this address was an input.

  7. 7.

    \(num\_transaction\_inputs\) – How many times this address has been used as a transaction input.

  8. 8.

    \(num\_siblings\) – How many unique addresses have been co-inputs or co-outputs with this address.

  9. 9.

    *\(num\_predecessor\_exchanges\) – How many unique exchange addresses have been an input to transactions where this address was an output.

  10. 10.

    *\(num\_successor\_exchanges\) – How many unique exchange addresses have been an output in transactions where this address was an input.

  11. 11.

    *\(num\_sibling\_exchanges\) – How many unique exchange addresses have been co-inputs or co-outputs with this address.

  12. 12.

    \(num\_gamma\_patterns\) – How many times this address is part of a \(\gamma \) pattern.

  13. 13.

    \(num\_beta\_patterns\) – How many times this address is part of a \(\beta \) pattern.

  14. 14.

    \(num\_L1\_patterns\) – How many times this address is part of an \(L_1\) pattern.

  15. 15.

    \(num\_L2\_patterns\) – How many times this address is part of an \(L_2\) pattern.

  16. 16.

    \(num\_alpha\_patterns\) – How many times this address is part of a \(\alpha \) pattern.

  17. 17.

    \(num\_alphaprime\_patterns\) – How many times this address is part of a \(\alpha '\) pattern.

  18. 18.

    reciprocity – How many of this addresses successors are also predecessors.

  19. 19.

    \(anti\_reciprocity\) – How many of this addresses predecessors are also successors.

We focused on local features that are fast to compute. Examples of more expensive but potentially very useful features are explained in [13], e.g. peeling chains or coinbase transactions.

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Ranshous, S. et al. (2017). Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph. In: Brenner, M., et al. Financial Cryptography and Data Security. FC 2017. Lecture Notes in Computer Science(), vol 10323. Springer, Cham. https://doi.org/10.1007/978-3-319-70278-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-70278-0_16

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-70278-0

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