Abstract
Coin mixing is a class of techniques used to enhance Bitcoin transaction privacy, and those well-performing coin mixing algorithms can effectively prevent most transaction analysis attacks. Based on this premise, to have a well-functioning transaction analysis algorithm requires coin mixing detection with a high recall to ensure accuracy. Most practical coin mixing algorithms do not change the Bitcoin protocol. Therefore, the transactions they generate are not fundamentally different from regular transactions. Existing coin mixing detection methods are commonly rule-based that can only identify coin mixing classes with well-defined patterns, which leads to an overall low recall rate. Multiple rules could improve the recall in this situation, yet they are ineffective for new classes and classes with ambiguous patterns. This paper considers coin mixing detection as a transaction classification problem and proposes an LSTM Transaction Tree Classifier (LSTM-TC) solution, which includes feature extraction and classification of Bitcoin transactions based on deep learning. We also build a dataset to validate our solution. Experiments show that our approach has better performance and the potential for discovering new classes of coin mixing transactions than rule-based approaches and graph neural network-based Bitcoin transaction classification algorithms.
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14 February 2022
A Correction to this paper has been published: https://doi.org/10.1007/s10489-022-03249-1
References
Bitcoin wiki (2010) Privacy. https://en.bitcoin.it/wiki/Privacy
Androulaki E, Karame GO, Roeschlin M, Scherer T, Capkun S (2013) Evaluating user privacy in bitcoin. In: International conference on financial cryptography and data security. Springer, pp 34–51
Arjun R, Suprabha K (2020) Innovation and challenges of blockchain in banking: a scientometric view International Journal of Interactive Multimedia & Artificial Intelligence 6(3)
Atlas K (2014) Coinjoin sudoku repository. https://github.com/kristovatlas/coinjoin-sudoku
Atlas K (2014) Weak privacy guarantees for sharedcoin mixing service
Atlas K (2015) Coinjoin sudoku http://www.coinjoinsudoku.com
Baek H, Oh J, Kim CY, Lee K (2019) A model for detecting cryptocurrency transactions with discernible purpose. In: 2019 Eleventh international conference on ubiquitous and future networks (ICUFN). IEEE, pp 713–717
Bissias G, Ozisik AP, Levine BN, Liberatore M (2014) Sybil-resistant mixing for bitcoin. In: Proceedings of the 13th workshop on privacy in the electronic society, pp 149–158
Bonneau J, Narayanan A, Miller A, Clark J, Kroll JA, Felten EW (2014) Mixcoin: Anonymity for bitcoin with accountable mixes. In: International conference on financial cryptography and data security. Springer, pp 486–504
Chepurnoy A, Saxena A (2020) Zerojoin: Combining zerocoin and coinjoin. In: Data privacy management, cryptocurrencies and blockchain technology. Springer, pp 421–436
Ermilov D, Panov M, Yanovich Y (2017) Automatic bitcoin address clustering. In: 2017 16th IEEE International conference on machine learning and applications (ICMLA). IEEE, pp 461–466
Fleder M, Kester MS, Pillai S (2015) Bitcoin transaction graph analysis. arXiv:1502.01657
Ivgi N (2019) Blockstream - esplora - privacy-analysis.js. https://github.com/Blockstream/esplora/blob/master/client/src/lib/priva cy-analysis.js
Maksutov AA, Alexeev MS, Fedorova NO, Andreev DA (2019) Detection of blockchain transactions used in blockchain mixer of coin join type. In: 2019 IEEE Conference of Russian young researchers in electrical and electronic engineering (EIConRus). IEEE, pp 274–277
Maurer FK, Neudecker T, Florian M (2017) Anonymous coinjoin transactions with arbitrary values. In: 2017 IEEE Trustcom/BigDataSE/ICESS. IEEE, pp 522–529
Maxwell G (2013) Coinjoin: Bitcoin privacy for the real world. https://bitcointalk.org/?topic=279249
Maxwell G (2013) I taint rich! (raw txn fun and disrupting ‘taint’ analysis;> 51kbtc linked!) https://bitcointalk.org/?topic=139581
Maxwell G (2013) Really really ultimate blockchain compression: Coinwitness https://bitcointalk.org/index.php?topic=277389
Meiklejohn S, Orlandi C (2015) Privacy-enhancing overlays in bitcoin. In: International conference on financial cryptography and data security. Springer, pp 127–141
Meiklejohn S, Pomarole M, Jordan G, Levchenko K, McCoy D, Voelker GM, Savage S (2013) A fistful of bitcoins: characterizing payments among men with no names. In: Proceedings of the 2013 conference on internet measurement conference, pp 127–140
Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. bitcoin.org
Ober M, Katzenbeisser S, Hamacher K (2013) Structure and anonymity of the bitcoin transaction graph. Future Internet 5(2):237–250
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. (2019) Pytorch: An imperative style, high-performance deep learning library. arXiv:1912.01703
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. (2011) Scikit-learn: Machine learning in python. J Mach Learn Res 12:2825–2830
Bitcoin Wiki contributors (2013) Bitcoin WIKI: Shared coin. https://en.bitcoin.it/wiki/Shared_coin last accessed: January 23, 2020
Reid F, Harrigan M (2013) An analysis of anonymity in the bitcoin system. In: Security and privacy in social networks. Springer, pp 197–223
Ron D, Shamir A (2013) Quantitative analysis of the full bitcoin transaction graph. In: International conference on financial cryptography and data security. Springer, pp 6–24
Ruffing T, Moreno-Sanchez P, Kate A (2014) Coinshuffle: Practical decentralized coin mixing for bitcoin. In: European symposium on research in computer security. Springer, pp 345–364
Sáez M (2020) Blockchain-enabled platforms: Challenges and recommendations. Int J Interactive Multimed Artif Intell 6(3)
ShenTu Q, Yu J (2015) Research on anonymization and de-anonymization in the bitcoin system. arXiv:1510.07782
Southurst J (2014) Blockchain’s sharedcoin users can be identified, says security expert. https://www.coindesk.com/blockchains-sharedcoin-users-can-identified-says-security-expert
Spagnuolo M, Maggi F, Zanero S (2014) Bitiodine: Extracting intelligence from the bitcoin network. In: International conference on financial cryptography and data security. Springer, pp 457–468
Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067–1077
Valenta L, Rowan B (2015) Blindcoin: Blinded, accountable mixes for bitcoin. In: International conference on financial cryptography and data security. Springer, pp 112–126
Weber M, Domeniconi G, Chen J, Weidele DKI, Bellei C, Robinson T, Leiserson CE (2019) Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv:1908.02591
Wu J, Yuan Q, Lin D, You W, Chen W, Chen C, Zheng Z (2020) Who are the phishers? phishing scam detection on ethereum via network embedding. IEEE Transactions on Systems, Man, and Cybernetics: Systems
Zheng B, Zhu L, Shen M, Du X, Guizani M (2020) Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering. Science China Information Sciences 63(3): 1–15
Ziegeldorf JH, Grossmann F, Henze M, Inden N, Wehrle K (2015) Coinparty: Secure multi-party mixing of bitcoins. In: Proceedings of the 5th ACM conference on data and application security and privacy, pp 75–86
Ziegeldorf JH, Matzutt R, Henze M, Grossmann F, Wehrle K (2018) Secure and anonymous decentralized bitcoin mixing. Futur Gener Comput Syst 80:448–466
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This work is supported by the National Key Research and Development Program of China under Grant No.2020YFE0200500
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The data mentioned in the article are available here: https://github.com/sxwxs/BitcoinCoinMixingDataSetWithRuleBasedLabel
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The dataset is available here: https://github.com/sxwxs/BitcoinCoinMixingDataSetWithRuleBasedLabel
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Sun, X., Yang, T. & Hu, B. LSTM-TC: Bitcoin coin mixing detection method with a high recall. Appl Intell 52, 780–793 (2022). https://doi.org/10.1007/s10489-021-02453-9
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DOI: https://doi.org/10.1007/s10489-021-02453-9