Abstract
Users in the Bitcoin system adopt a pseudonym-Bitcoin address as the transaction account, making Bitcoin address correlation analysis a challenging task. Under this circumstance, this paper provides a new Bitcoin address association scheme which makes address tracing possible in Bitcoin systems. The proposed scheme can be used to warn relevant institutions to study more secure encryption algorithms to protect users’ privacy. Specifically, the important features of a Bitcoin address are extracted. After that, to reduce the computational complexity, we transform the clustering problem of addresses into a binary classification problem in which we integrate the features of two Bitcoin addresses. A novel two-level learner model is then built to analyze if the two Bitcoin addresses are belonging to the same user. Finally we cluster the addresses belonging to the same user accordingly. Extensive experimental results show that the proposed method outperforms the other address association schemes, which use deep learning models or heuristics, and can achieve an increase by 6%–20% in precision and by 10% improvement in recall.
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References
ShenTu, Q.C., Yu, J.P.: Research on anonymization and de-anonymization in the bitcoin system. arXiv preprint arXiv:1510.07782 (2015)
Brenig, C., Accorsi, R., Müller, G.: Economic analysis of cryptocurrency backed money laundering. In: ECIS (2015)
Fanusie, Y., Robinson, T.: Bitcoin laundering: an analysis of illicit flows into digital currency services. Center on Sanctions & Illicit Finance memorandum, January 2018
Liao, K., Zhao, Z., et al.: Behind closed doors: measurement and analysis of CryptoLocker ransoms in bitcoin. In: 2016 APWG Symposium on Electronic Crime Research (eCrime), pp. 1–13. IEEE (2016)
Meiklejohn, S., Pomarole, M., et al.: A fistful of bitcoins: characterizing payments among men with no name. In: Proceedings of the 2013 Conference on Internet Measurement Conference, pp. 127–140. ACM (2013)
Fleder, M., Kester, M.S., Pillai, S.: Bitcoin transaction graph analysis. arXiv preprint arXiv:1502.01657 (2015)
Ermilov, D., Panov, M., Yanovich, Y.: Automatic bitcoin address clustering. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 461–466. IEEE (2017)
Nick, J.D.: Data-driven de-anonymization in bitcoin. Master’s thesis, ETH-Zürich (2015)
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). https://doi.org/10.1007/978-1-4614-4139-7_10
Ron, D., Shamir, A.: How did dread pirate roberts acquire and protect his bitcoin wealth? In: Böhme, R., Brenner, M., Moore, T., Smith, M. (eds.) FC 2014. LNCS, vol. 8438, pp. 3–15. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44774-1_1
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
Kaminsky, D.: Black ops of TCP/IP. Black Hat USA, p. 44 (2011)
Biryukov, A., Pustogarov, I.: Bitcoin over tor isn’t a good idea. In: 2015 IEEE Symposium on Security and Privacy, pp. 122–134. IEEE (2015)
Mastan, I.D., Paul, S.: A new approach to deanonymization of unreachable bitcoin nodes. In: Capkun, S., Chow, S.S.M. (eds.) CANS 2017. LNCS, vol. 11261, pp. 277–298. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02641-7_13
Shao, W., Li, H., Chen, M., Jia, C., Liu, C., Wang, Z.: Identifying bitcoin users using deep neural network. In: Vaidya, J., Li, J. (eds.) ICA3PP 2018. LNCS, vol. 11337, pp. 178–192. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05063-4_15
Sanjaya, C., et al.: Revenue prediction using artificial neural network. In: 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 97–99. IEEE (2010)
Bartoletti, M., et al.: Data mining for detecting Bitcoin Ponzi schemes. In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 75–84. IEEE (2018)
Ghahramani, Z.: Unsupervised learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) ML -2003. LNCS (LNAI), vol. 3176, pp. 72–112. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28650-9_5
Fan, X., Hongbo, X., Liang, Y.: A sock-puppet relation detection method on social network. J. Chin. Inf. Process. 28(6), 162–168 (2014)
LeCun, Y., Bengio, Y.: Deep learning. Nature 521(7553), 436 (2015)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
Ke, G., Meng, Q., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146–3154 (2017)
A guide to model stacking in practice (2016). http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)
Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML, vol. 2 (2016)
mtgox2014leak. https://www.reddit.com/r/mtgoxAddresses/wiki/mtgox2014leak (2014)
Chen, W., Wu, J., Zheng, Z., Chen, C., Zhou, Y.: Market manipulation of bitcoin: evidence from mining the Mt. Gox transaction network. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 964–972. IEEE (2019)
Xing, Y., Li, X., et al.: Research on de-anonymization techniques of bitcoin trading network. A Thesis Submitted to Southeast University For the Academic Degree of Master of Engineering, China (2017)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Consulted (2008)
Blockchain data API. https://www.blockchain.com/zh/api/blockchain_api (2017)
Arlot, S., Celisse, A., et al.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)
Pearson correlation coefficient. https://en.wikipedia.org/wiki/Pearson_correlation_coefficien (2019)
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Liu, T. et al. (2020). A New Bitcoin Address Association Method Using a Two-Level Learner Model. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_31
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