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Address Clustering Heuristics for Ethereum

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Financial Cryptography and Data Security (FC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12059))

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Abstract

For many years, address clustering for the identification of entities has been the basis for a variety of graph-based investigations of the Bitcoin blockchain and its derivatives. Especially in the field of fraud detection it has proven to be useful. With the popularization and increasing use of alternative blockchains, the question arises how to recognize entities in these new systems. Currently, there are no heuristics that can directly be applied to Ethereum’s account balance model. This drawback also applies to other smart contract platforms like EOS or NEO, for which previous transaction network analyses have been limited to address graphs. In this paper, we show how addresses can be clustered in Ethereum, yielding entities that are likely in control of multiple addresses. We propose heuristics that exploit patterns related to deposit addresses, multiple participation in airdrops and token authorization mechanisms. We quantify the applicability of each individual heuristic over the first 4 years of the Ethereum blockchain and illustrate identified entities in a sample token network. Our results show that we can cluster 17.9% of all active externally owned account addresses, indicating that there are more than 340,000 entities that are likely in control of multiple addresses. Comparing the heuristics, we conclude that the deposit address heuristic is currently the most effective approach.

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Notes

  1. 1.

    https://github.com/etherclust/etherclust.

  2. 2.

    https://eips.ethereum.org/EIPS/eip-20.

  3. 3.

    https://eips.ethereum.org/EIPS/eip-721.

  4. 4.

    https://etherscan.io/accounts/label/exchange.

References

  1. 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). https://doi.org/10.1007/978-3-642-39884-1_4

    Chapter  Google Scholar 

  2. Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting bitcoin ponzi schemes. In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 75–84. IEEE (2018)

    Google Scholar 

  3. Bartoletti, M., Pompianu, L.: An empirical analysis of smart contracts: platforms, applications, and design patterns. In: Brenner, M., et al. (eds.) FC 2017. LNCS, vol. 10323, pp. 494–509. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70278-0_31

    Chapter  Google Scholar 

  4. Bissias, G., Ozisik, A.P., Levine, B.N., Liberatore, M.: Sybil-resistant mixing for bitcoin. In: Proceedings of the 13th Workshop on Privacy in the Electronic Society, pp. 149–158. ACM (2014)

    Google Scholar 

  5. Chen, T., et al.: Understanding ethereum via graph analysis. In: IEEE International Conference on Computer Communications, pp. 1484–1492. IEEE (2018)

    Google Scholar 

  6. Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., Zhou, Y.: Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: Proceedings of the 2018 World Wide Web Conference, pp. 1409–1418. International World Wide Web Conferences Steering Committee (2018)

    Google Scholar 

  7. Fanusie, Y.J., Robinson, T.: Bitcoin laundering: an analysis of illicit flows into digital currency services. A memorandum by the Center on Sanctions and Illicit Finance and Elliptic, January 2018

    Google Scholar 

  8. Ferretti, S., D’Angelo, G.: On the ethereum blockchain structure: a complex networks theory perspective. Concurr. Comput.: Pract. Exp. 32, e5493 (2020)

    Google Scholar 

  9. Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: 2016 International IEEE Conferences 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 

  10. Harrigan, M., Shi, L., Illum, J.: Airdrops and privacy: a case study in cross-blockchain analysis. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 63–70. IEEE (2018)

    Google Scholar 

  11. Haslhofer, B., Karl, R., Filtz, E.: O bitcoin where art thou? Insight into large-scale transaction graphs. In: SEMANTiCS (Posters, Demos) (2016)

    Google Scholar 

  12. Jourdan, M., Blandin, S., Wynter, L., Deshpande, P.: Characterizing entities in the bitcoin blockchain. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 55–62. IEEE (2018)

    Google Scholar 

  13. Kalodner, H., Goldfeder, S., Chator, A., Möser, M., Narayanan, A.: BlockSci: design and applications of a blockchain analysis platform. arXiv preprint arXiv:1709.02489 (2017)

  14. Kappos, G., Yousaf, H., Maller, M., Meiklejohn, S.: An empirical analysis of anonymity in Zcash. In: 27th USENIX Security Symposium, USENIX Security 2018, pp. 463–477 (2018)

    Google Scholar 

  15. Madore, P.H.: Crypto Market OpenBazaar Confirms Upcoming Support for Ethereum (2019). https://www.ccn.com/openbazaar-adding-support-ethereum-soon/. Accessed 12 Sept 2019

  16. Maxwell, G.: CoinJoin: Bitcoin privacy for the real world (2013). bitcointalk.org/index.php?topic=279249. Accessed 12 Sept 2019

  17. Meiklejohn, S., Mercer, R.: Möbius: trustless tumbling for transaction privacy. In: Proceedings on Privacy Enhancing Technologies, pp. 105–121 (2018)

    Google Scholar 

  18. Meiklejohn, S., et al.: A fistful of bitcoins: characterizing payments among men with no names. In: Proceedings of the Internet Measurement Conference - IMC 2013, no. 6, pp. 127–140 (2013)

    Google Scholar 

  19. Monaco, J.V.: Identifying bitcoin users by transaction behavior. In: Biometric and Surveillance Technology for Human and Activity Identification XII, vol. 9457, p. 945704. International Society for Optics and Photonics (2015)

    Google Scholar 

  20. Moreno-Sanchez, P., Zafar, M.B., Kate, A.: Listening to whispers of ripple: linking wallets and deanonymizing transactions in the ripple network. In: Proceedings on Privacy Enhancing Technologies, no. 4, pp. 436–453 (2016)

    Google Scholar 

  21. Möser, M., Böhme, R., Breuker, D.: An inquiry into money laundering tools in the bitcoin ecosystem. In: 2013 APWG eCrime Researchers Summit, pp. 1–14. IEEE (2013)

    Google Scholar 

  22. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)

    Google Scholar 

  23. Neudecker, T., Hartenstein, H.: Could network information facilitate address clustering in bitcoin? In: Brenner, M., et al. (eds.) FC 2017. LNCS, vol. 10323, pp. 155–169. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70278-0_9

    Chapter  Google Scholar 

  24. Ober, M., Katzenbeisser, S., Hamacher, K.: Structure and anonymity of the bitcoin transaction graph. Future Internet 2, 237–250 (2013)

    Article  Google Scholar 

  25. Paquet-Clouston, M., Haslhofer, B., Dupont, B.: Ransomware payments in the bitcoin ecosystem. J. Cybersecur. 5(1), 1–11 (2019). tyz003

    Article  Google Scholar 

  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). https://doi.org/10.1007/978-1-4614-4139-7_10

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-642-39884-1_2

    Chapter  Google Scholar 

  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). https://doi.org/10.1007/978-3-319-11212-1_20

    Chapter  Google Scholar 

  29. Seres, I.A., Nagy, D.A., Buckland, C., Burcsi, P.: MixEth: efficient, trustless coin mixing service for ethereum. IACR Cryptology ePrint Archive, p. 341 (2019)

    Google Scholar 

  30. Somin, S., Gordon, G., Altshuler, Y.: Network analysis of ERC20 tokens trading on ethereum blockchain. In: Morales, A.J., Gershenson, C., Braha, D., Minai, A.A., Bar-Yam, Y. (eds.) ICCS 2018. SPC, pp. 439–450. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96661-8_45

    Chapter  Google Scholar 

  31. 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). https://doi.org/10.1007/978-3-662-45472-5_29

    Chapter  Google Scholar 

  32. Victor, F., Lüders, B.K.: Measuring ethereum-based ERC20 token networks. In: Goldberg, I., Moore, T. (eds.) FC 2019. LNCS, vol. 11598, pp. 113–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32101-7_8

    Chapter  Google Scholar 

  33. Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. https://github.com/ethereum/yellowpaper

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Authors and Affiliations

  1. Technical University of Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany

    Friedhelm Victor

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  1. Friedhelm Victor
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Correspondence to Friedhelm Victor .

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Editors and Affiliations

  1. New York University, New York City, NY, USA

    Joseph Bonneau

  2. University of California, La Jolla, CA, USA

    Nadia Heninger

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Victor, F. (2020). Address Clustering Heuristics for Ethereum. In: Bonneau, J., Heninger, N. (eds) Financial Cryptography and Data Security. FC 2020. Lecture Notes in Computer Science(), vol 12059. Springer, Cham. https://doi.org/10.1007/978-3-030-51280-4_33

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  • DOI: https://doi.org/10.1007/978-3-030-51280-4_33

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