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Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering

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Abstract

The anonymity mechanism of bitcoin is favored by the society, which promotes its usage and development. An adversary should not be able to discover the relation between bitcoin addresses and bitcoin users to ensure effective privacy. However, the relation among bitcoin transactions can be used to analyze the bitcoin privacy information, which seriously jeopardizes the bitcoin anonymity. Herein, we describe the vulnerabilities associated with the anonymity mechanism of bitcoin, including the relation among bitcoin addresses and the relation among bitcoin users. Further, we demonstrate that the existing methods do not guarantee the comprehensiveness, accuracy, and efficiency of the analysis results. We propose a heuristic clustering method to judge the relation among bitcoin addresses and employ the Louvain method to discover the relation among bitcoin users. Subsequently, we construct an address-associated database of historical transactions and implement real-time updates. Extensive experiments are used to demonstrate the comprehensiveness, accuracy, and efficiency of the proposed scheme. Specifically, the proposed scheme reveals the privacy vulnerability associated with the blockchain technology. We expect that our scheme can be applied to improve the blockchain technology.

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References

  1. Nakamoto S. Bitcoin: A Peer-to-Peer Electronic Cash System. Technical Report, 2008. https://bitcoin.org/bitcoin.pdf

    Google Scholar 

  2. Androulaki E, Karame G O, Roeschlin M, et al. Evaluating user privacy in bitcoin. In: Proceedings of International Conference on Financial Cryptography and Data Security, Berlin, 2013. 34–51

    Chapter  Google Scholar 

  3. Liao K, Zhao Z M, Doupe A, et al. Behind closed doors: measurement and analysis of cryptolocker ransoms in bitcoin. In: Proceedings of Symposium on Electronic Crime Research, Toronto, 2016

    Google Scholar 

  4. Meiklejohn S, Pomarole M, Jordan G, et al. A fistful of bitcoins: characterizing payments among men with no names. In: Proceedings of Conference on Internet Measurement Conference, New York, 2013. 127–140

    Google Scholar 

  5. Monaco J V. Identifying bitcoin users by transaction behavior. In: Proceedings of Biometric and Surveillance Technology for Human and Activity Identification XII, Baltimore, 2015. 945704

    Google Scholar 

  6. Reid F, Harrigan M. An analysis of anonymity in the bitcoin system. In: Proceedings of IEEE International Conference on Social Computing (SocialCom), Boston, 2011. 1318–1326

    Google Scholar 

  7. Ron D, Shamir A. Quantitative analysis of the full bitcoin transaction graph. In: Proceedings of Financial Cryptography and Data Security, Okinawa, 2013. 6–24

    Chapter  Google Scholar 

  8. Spagnuolo M, Maggi F, Zanero S. BitIodine: extracting intelligence from the bitcoin network. In: Proceedings of International Conference on Financial Cryptography & Data Security, Christ Church, 2014. 457–468

    Google Scholar 

  9. Zhao C, Guan Y. A graph-based investigation of bitcoin transactions. In: Proceedings of IFIP International Conference on Digital Forensics, Orlando, 2015. 79–95

    Google Scholar 

  10. Zheng B K, Zhu L H, Shen M, et al. Scalable and privacy-preserving data sharing based on blockchain. J Comput Sci Technol, 2018, 33: 557–567

    Article  MathSciNet  Google Scholar 

  11. Zheng B K, Zhu L H, Shen M, et al. Malicious bitcoin transaction tracing using incidence relation clustering. In: Proceedings of International Conference on Mobile Networks & Management, Melbourne, 2017. 313–323

    Google Scholar 

  12. Gao F, Zhu L H, Shen M, et al. A blockchain-based privacy-preserving payment mechanism for vehicle-to-grid networks. IEEE Netw, 2018, 32: 184–192

    Article  Google Scholar 

  13. Antonopoulos A M. Mastering Bitcoin: Unlocking Digital Crypto-Currencies. Sebastopol: O’Reilly Media, 2014

    Google Scholar 

  14. Swan M. Blockchain: Blueprint for a New Economy. Sebastopol: O’Reilly Media, 2015

    Google Scholar 

  15. Saxena A, Misra J, Dhar A. Increasing anonymity in bitcoin. In: Proceedings of International Conference on Financial Cryptography & Data Security, Christ Church, 2014. 122–139

    Google Scholar 

  16. Neilson D, Hara S, Mitchell I. Bitcoin forensics: a tutorial. In: Proceedings of International Conference on Global Security, London, 2017. 12–26

    Google Scholar 

  17. Li H Y, Zhu L H, Shen M, et al. Blockchain-based data preservation system for medical data. J Med Syst, 2018, 42: 141

    Article  Google Scholar 

  18. Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks. J Stat Mech, 2008, 2008: 10008

    Article  Google Scholar 

  19. Lewenberg Y, Bachrach Y, Sompolinsky Y, et al. Bitcoin mining pools: a cooperative game theoretic analysis. In: Proceedings of International Conference on Autonomous Agents and Multiagent Systems, Istanbul, 2015. 919–927

    Google Scholar 

  20. Gach O, Hao J K. Improving the Louvain algorithm for community detection with modularity maximization. In: Proceedings of Artificial Evolution, Bordeaux, 2013. 145–156

    Google Scholar 

  21. Park H S, Jun C H. A simple and fast algorithm for K-medoids clustering. Expert Syst Appl, 2009, 36: 3336–3341

    Article  Google Scholar 

  22. Du X J, Chen H H. Security in wireless sensor networks. IEEE Wirel Commun, 2008, 15: 60–66

    Google Scholar 

  23. Zhang C, Zhu L H, Xu C. PTBI: an efficient privacy-preserving biometric identification based on perturbed term in the cloud. Inf Sci, 2017, 409: 56–67

    Article  Google Scholar 

  24. Du X J, Xiao Y, Guizani M, et al. An effective key management scheme for heterogeneous sensor networks. Ad Hoc Netw, 2007, 5: 24–34

    Article  Google Scholar 

  25. Fahad A, Alshatri N, Tari Z, et al. A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans Emerg Top Comput, 2014, 2: 267–279

    Article  Google Scholar 

  26. Zhang C, Zhu L H, Xu C, et al. PPDP: an efficient and privacy-preserving disease prediction scheme in cloud-based e-Healthcare system. Future Generation Comput Syst, 2018, 79: 16–25

    Article  Google Scholar 

  27. Guan Z T, Si G L, Zhang X S, et al. Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities. IEEE Commun Mag, 2018, 56: 82–88

    Article  Google Scholar 

  28. Guan Z T, Zhang Y, Zhu L H, et al. EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid. Sci China Inf Sci, 2019, 62: 032103

    Article  Google Scholar 

  29. Shen M, Ma B L, Zhu L H, et al. Secure phrase search for intelligent processing of encrypted data in cloud-based IoT. IEEE Int Thing J, 2019, 6: 1998–2008

    Article  Google Scholar 

  30. Shen M, Ma B L, Zhu L H, et al. Cloud-based approximate constrained shortest distance queries over encrypted graphs with privacy protection. IEEE Trans Inform Forensic Secur, 2018, 13: 940–953

    Article  Google Scholar 

  31. Shen M, Tang X Y, Zhu L H, et al. Privacy-preserving support vector machine training over blockchain-based encrypted iot data in smart cities. IEEE Int Thing J, 2019. doi: 10.1109/JIOT.2019.2901840

    Google Scholar 

  32. de Meo P, Ferrara E, Fiumara G, et al. Generalized Louvain method for community detection in large networks. In: Proceedings of International Conference on Intelligent Systems Design and Applications, Córdoba, 2011. 88–93

    Google Scholar 

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Acknowledgements

This work was supported by National Cryptography Development Fund (GrantNo.MMJJ20180412).

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Baokun Zheng, Liehuang Zhu & Meng Shen

  2. School of Information Management for Law, China University of Political Science and Law, Beijing, 102249, China

    Baokun Zheng

  3. Department of Computer and Information Sciences, Temple University, Philadelphia, 19122-6008, USA

    Xiaojiang Du

  4. Department of Computer Science and Engineering, Qatar University, Doha, 2713, Qatar

    Mohsen Guizani

Authors
  1. Baokun Zheng
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  2. Liehuang Zhu
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  3. Meng Shen
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  4. Xiaojiang Du
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  5. Mohsen Guizani
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Corresponding authors

Correspondence to Liehuang Zhu or Meng Shen.

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Zheng, B., Zhu, L., Shen, M. et al. Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering. Sci. China Inf. Sci. 63, 132101 (2020). https://doi.org/10.1007/s11432-019-9900-9

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  • DOI: https://doi.org/10.1007/s11432-019-9900-9

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