Identifying Bitcoin Users Using Deep Neural Network

  • Wei Shao
  • Hang Li
  • Mengqi Chen
  • Chunfu JiaEmail author
  • Chunbo Liu
  • Zhi Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)


In Bitcoin user identification, an important challenge is to accurately link Bitcoin addresses to their owners. Previously, some heuristics based on transaction structural rules or observations were found and used for Bitcoin address clustering. In this paper, we propose a deep learning method to achieve address-user mapping. We define addresses by their transactional behaviors and seek concealed patterns and characteristics of users that can help us distinguish the owner of a certain address from millions of others.

We propose a system that learns a mapping from address representations to a compact Euclidean space where distances directly correspond to a measure of address similarity. We train a deep neural network for address behavior embedding and optimization to finally obtain an address feature vector for each address. We identify owners of addresses through address verification, recognition and clustering, where the implementation relies directly on the distance between address feature vectors.

We set up an address-user pairing dataset with extensive collections and careful sanitation. We tested our method using the dataset and proved its efficiency. In contrast to heuristic-based methods, our model shows great performance in Bitcoin user identification.


Bitcoin Blockchain Deep learning Bitcoin privacy 


  1. 1.
  2. 2.
    Bitcoin blockchain info.
  3. 3.
    Bitcoin blockchain info tags.
  4. 4.
    Bitcoin whos who.
  5. 5.
    Androulaki, E., Karame, G.O., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 34–51. Springer, Heidelberg (2013). Scholar
  6. 6.
    Biryukov, A., Khovratovich, D., Pustogarov, I.: Deanonymisation of clients in bitcoin P2P network. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 15–29. ACM (2014)Google Scholar
  7. 7.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv Preprint ArXiv:1406.1078 (2014)
  8. 8.
    Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198. ACM (2016)Google Scholar
  9. 9.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)Google Scholar
  10. 10.
    Fleder, M., Kester, M.S., Pillai, S.: Bitcoin transaction graph analysis. ArXiv Preprint ArXiv:1502.01657 (2015)
  11. 11.
    Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: International Conference on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 368–373. IEEE (2016)Google Scholar
  12. 12.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  13. 13.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst (2007)Google Scholar
  14. 14.
    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
  15. 15.
    Koshy, P., Koshy, D., McDaniel, P.: An analysis of anonymity in bitcoin using P2P network traffic. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 469–485. Springer, Heidelberg (2014). Scholar
  16. 16.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  17. 17.
    Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE (2017)Google Scholar
  18. 18.
    Maesa, D.D.F., Marino, A., Ricci, L.: Data-driven analysis of bitcoin properties: exploiting the users graph. Int. J. Data Sci. Anal., pp. 1–18 (2017)Google Scholar
  19. 19.
    Manning, C.D., Raghavan, P., Schtze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  20. 20.
    Meiklejohn, S., et al.: 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
  21. 21.
    Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., Joulin, A.: Advances in pre-training distributed word representations. ArXiv Preprint ArXiv:1712.09405 (2017)
  22. 22.
    Mikolov, T., Karafiát, M., Burget, L., Černockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Eleventh Annual Conference of the International Speech Communication Association (2010)Google Scholar
  23. 23.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Consulted (2008)Google Scholar
  24. 24.
    Nick, J.D.: Data-driven de-anonymization in bitcoin. Master’s thesis, ETH-Zürich (2015)Google Scholar
  25. 25.
    Ober, M., Katzenbeisser, S., Hamacher, K.: Structure and anonymity of the bitcoin transaction graph. Future Internet 5(2), 237–250 (2013)CrossRefGoogle Scholar
  26. 26.
    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). Scholar
  27. 27.
    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). Scholar
  28. 28.
    Ruffing, T., Moreno-Sanchez, P., Kate, A.: CoinShuffle: practical decentralized coin mixing for bitcoin. In: Kutyłowski, M., Vaidya, J. (eds.) ESORICS 2014. LNCS, vol. 8713, pp. 345–364. Springer, Cham (2014). Scholar
  29. 29.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823. IEEE (2015)Google Scholar
  30. 30.
    Spagnuolo, M., Maggi, F., Zanero, S.: BitIodine: extracting intelligence from the bitcoin network. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 457–468. Springer, Heidelberg (2014). Scholar
  31. 31.
    Wang, F., Liu, W., Liu, H., Cheng, J.: Additive margin softmax for face verification. ArXiv Preprint ArXiv:1801.05599 (2018)
  32. 32.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). Scholar
  33. 33.
    Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 529–534. IEEE (2011)Google Scholar
  34. 34.
    Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5409–5418. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wei Shao
    • 1
  • Hang Li
    • 1
  • Mengqi Chen
    • 1
  • Chunfu Jia
    • 1
    Email author
  • Chunbo Liu
    • 2
  • Zhi Wang
    • 1
  1. 1.College of Cyberspace SecurityNankai UniversityTianjinChina
  2. 2.Information Security Evaluation Center of Civil AviationCivil Aviation University of ChinaTianjinChina

Personalised recommendations