Análise do impacto do uso de Mixers no Clustering H1

• Maria Rita Vieira Souza UFU
• Ivan da Silva Sendin UFU

DOI: https://doi.org/10.5753/sbseg_estendido.2026.29470

Resumo

A análise forense do Bitcoin tem como objetivo a detecção de crimes e a identificação dos criminosos usando as informações presentes na Blockchain. Para isso, um método amplamente empregado é o clustering baseado na H1, que auxilia na identificação dos responsáveis pelos endereços publicamente registrados na rede. Contudo, a aplicação de mixers, ferramentas usadas no processo de lavagem de dinheiro para mascarar o rastro financeiro, invalida a premissa dessa heurística e gera clusters incorretos de entidades. Dessa forma, este trabalho visa analisar e mensurar o impacto do uso de mixers nos resultados obtidos pela H1 e, consequentemente, na precisão dessas investigações.

Referências

Androulaki, E., Karame, G. O., Roeschlin, M., Scherer, T., and Capkun, S. (2013). Evaluating user privacy in bitcoin. In Sadeghi, A.-R., editor, Financial Cryptography and Data Security, pages 34–51, Berlin, Heidelberg. Springer Berlin Heidelberg.

Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking Digital Crypto-Currencies. O’Reilly Media, Inc., 1st edition.

Ficsór, , Seres, I., Kogman, Y., and Ontivero, L. (2021). Wabisabi: Centrally coordinated coinjoins with variable amounts. Cryptoeconomic Systems.

Gong, Y., Chow, K.-P., Ting, H.-F., and Yiu, S.-M. (2022). Analyzing the error rates of bitcoin clustering heuristics. In IFIP International Conference on Digital Forensics, pages 187–205. Springer.

Harrigan, M. and Fretter, C. (2016). The unreasonable effectiveness of address clustering. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence amp; 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), page 368–373. IEEE.

Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., and Savage, S. (2013). A fistful of bitcoins: Characterizing payments among men with no names. In Proceedings of the 2013 Conference on Internet Measurement Conference, IMC ’13, pages 127–140, New York, NY, USA. ACM.

Möser, M. and Narayanan, A. (2022). Resurrecting address clustering in bitcoin. In Eyal, I. and Garay, J., editors, Financial Cryptography and Data Security, pages 386–403, Cham. Springer International Publishing.

Möser, M. and Böhme, R. (2017). The price of anonymity: Empirical evidence from a market for bitcoin anonymization. Journal of Cybersecurity, 3.

Nakamoto, S. (2009). Bitcoin: A peer-to-peer electronic cash system.

Patsakis, C., Politou, E., Alepis, E., and Hernandez-Castro, J. (2024). Cashing out crypto: state of practice in ransom payments. International Journal of Information Security, 23:699–712.

Schnoering, H., Porthaux, P., and Vazirgiannis, M. (2024). Assessing the efficacy of heuristic-based address clustering for bitcoin. ArXiv, abs/2403.00523.

Schnoering, H. and Vazirgiannis, M. (2023). Heuristics for detecting coinjoin transactions on the bitcoin blockchain. ArXiv, abs/2311.12491.

Schnoering, H. and Vazirgiannis, M. (2024). Bitcoin research with a transaction graph dataset.

Sendin, I. d. S., Miani, R. S., and Ribeiro, P. H. R. (2024). Análise Forense aplicada ao Bitcoin, chapter 4, pages 132–179. Minicursos do XXIV Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais.

Stockinger, J., Haslhofer, B., Moreno-Sanchez, P., and Maffei, M. (2021). Pinpointing and measuring wasabi and samourai coinjoins in the bitcoin ecosystem. ArXiv, abs/2109.10229.

Stütz, R., Stockinger, J., Moreno-Sanchez, P., Haslhofer, B., and Maffei, M. (2023). Adoption and actual privacy of decentralized coinjoin implementations in bitcoin. In Proceedings of the 4th ACM Conference on Advances in Financial Technologies, AFT ’22, page 254–267, New York, NY, USA. Association for Computing Machinery.

Svenda, P., Gavenda, J., Mavroudis, V., and Hicks, C. (2026). Coinjoin ecosystem insights for wasabi 1.x, wasabi 2.x and whirlpool coordinator-based privacy mixers. Proceedings on Privacy Enhancing Technologies, 2026:557–592.

Todd, P., Wuille, P., and Johnson, J. (2015). Bip 141: Segregated witness (consensus layer). [link].

van Wegberg, R., Oerlemans, J. J., and van Deventer, O. (2018). Bitcoin money laundering: mixed results?: An explorative study on money laundering of cybercrime proceeds using bitcoin. Journal of Financial Crime, 25:419–435.

Ziegler, M. H., Nowostawski, M., and Katt, B. (2025). A similarity measure for linking coinjoin output spenders. Journal of Cybersecurity and Privacy, 5(4).

Zola, F., Medina, J. A., Venturi, A., and Orduna, R. (2025). Topological analysis of mixer activities in the bitcoin network. 2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pages 1–5.