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Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph

  • Stephen RanshousEmail author
  • Cliff A. Joslyn
  • Sean Kreyling
  • Kathleen Nowak
  • Nagiza F. Samatova
  • Curtis L. West
  • Samuel Winters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10323)

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.

Keywords

Bitcoin Exchanges Transaction graph Directed hypergraph Motif Classification 

Notes

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.

Supplementary material

References

  1. 1.
    Anti-money laundering programs for money services businesses, 31 C.F.R. § 1022.210Google Scholar
  2. 2.
    Compliance and exemptions, and summons authority, 31 U.S.C. §5318Google Scholar
  3. 3.
    Ausiello, G., Franciosa, P.G., Frigioni, D.: Directed hypergraphs: problems, algorithmic results, and a novel decremental approach. ICTCS 2001. LNCS, vol. 2202, pp. 312–328. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45446-2_20 CrossRefGoogle Scholar
  4. 4.
    FATF: Virtual currencies key definitions and potential AML/CFT risks. Technical report (2014)Google Scholar
  5. 5.
    Gallo, G., Longo, G., Pallottino, S.: Directed hypergraphs and applications. Discret. Appl. Math. 42, 177–201 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Kondor, D., Pósfai, M., Csabai, I., Vattay, G.: Do the rich get richer? an empirical analysis of the bitcoin transaction network. PloS one 9(2), e86197 (2014)CrossRefGoogle Scholar
  7. 7.
    Li, X.-L., Liu, B.: Learning from positive and unlabeled examples with different data distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS, vol. 3720, pp. 218–229. Springer, Heidelberg (2005).  https://doi.org/10.1007/11564096_24 CrossRefGoogle Scholar
  8. 8.
    Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially supervised classification of text documents. In: ICML, vol. 2, pp. 387–394. Citeseer (2002)Google Scholar
  9. 9.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  10. 10.
    Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G.M., Savage, S.: A fistful of bitcoins: characterizing payments among men with no names. In: Proceedings of the 2013 conference on Internet measurement conference. pp. 127–140. ACM (2013)Google Scholar
  11. 11.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskil, D., Alon, U.: Network motifs: simlpe building blocks of complex networks. Science 298, 824–827 (2002)CrossRefGoogle Scholar
  12. 12.
    Möser, M., Böhme, R., Breuker, D.: An inquiry into money launder tools in the bitcoin ecosystem. In: eCrime Researchers Summit, 6–24. Springer, Heidelberg (2013)Google Scholar
  13. 13.
    Möser, M., Böhme, R., Breuker, D.: Towards risk scoring of bitcoin transactions. In: Böhme, R., Brenner, M., Moore, T., Smith, M. (eds.) FC 2014. LNCS, vol. 8438, pp. 16–32. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44774-1_2 Google Scholar
  14. 14.
    Ober, M., Katzenbeisser, S., Hamacher, K.: Structure and anonymity of the bitcoin transaction graph. Future Internet 5(2), 237–250 (2013)CrossRefGoogle Scholar
  15. 15.
    Reid, F., Harrigan, M.: An analysis of anonymity in the bitcoin system. In: Altshuler, Y., Elovici, Y., Cremers, A., Aharony, N., Pentland, A. (eds.) Security and Privacy in Social Networks, pp. 197–223. Springer, New York (2013)CrossRefGoogle Scholar
  16. 16.
    Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 6–24. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39884-1_2 CrossRefGoogle Scholar
  17. 17.
    U.S. Department of the Treasury, FinCEN: FinCEN fines ripple labs Inc.: First Civil Enforcement Action Against A Virtual Currency Exchanger. May 2015. https://www.fincen.gov/news/news-releases/fincen-fines-ripple-labs-inc-first-civil-enforcement-action-against-virtual

Copyright information

© International Financial Cryptography Association 2017

Authors and Affiliations

  • Stephen Ranshous
    • 1
    Email author
  • Cliff A. Joslyn
    • 2
  • Sean Kreyling
    • 2
  • Kathleen Nowak
    • 4
  • Nagiza F. Samatova
    • 1
    • 3
  • Curtis L. West
    • 2
  • Samuel Winters
    • 2
  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.Pacific Northwest National LaboratorySeattle, WAUSA
  3. 3.Oak Ridge National LaboratoryOak RidgeUSA
  4. 4.Pacific Northwest National LaboratoryRichlandUSA

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