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SCIENCE CHINA Information Sciences
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SCIENCE CHINA Information Sciences, Volume 63 , Issue 3 : 132101(2020) https://doi.org/10.1007/s11432-019-9900-9

Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering

Baokun ZHENG 1,2, Liehuang ZHU 1,*, Meng SHEN 1,*, Xiaojiang DU 3, Mohsen GUIZANI 4
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  • ReceivedApr 26, 2019
  • AcceptedMay 21, 2019
  • PublishedFeb 11, 2020
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Abstract


Acknowledgment

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


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. 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] Kakadiaris I A, Kumar A, Scheirer W J, et al. 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. 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. Scalable and Privacy-Preserving Data Sharing Based on Blockchain. J Comput Sci Technol, 2018, 33: 557-567 CrossRef 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, Shen M. A Blockchain-Based Privacy-Preserving Payment Mechanism for Vehicle-to-Grid Networks. IEEE Network, 2018, 32: 184-192 CrossRef 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, Zhu L, Shen M. Blockchain-Based Data Preservation System for Medical Data.. J Med Syst, 2018, 42: 141 CrossRef PubMed Google Scholar

[18] Blondel V D, Guillaume J L, Lambiotte R. Fast unfolding of communities in large networks. J Stat Mech, 2008, 2008(10): P10008 CrossRef ADS arXiv 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 CrossRef Google Scholar

[22] Xiaojiang Du , Hsiao-Hwa Chen . Security in wireless sensor networks. IEEE Wireless Commun, 2008, 15: 60-66 CrossRef Google Scholar

[23] Zhang C, Zhu L, Xu C. PTBI: An efficient privacy-preserving biometric identification based on perturbed term in the cloud. Inf Sci, 2017, 409-410: 56-67 CrossRef Google Scholar

[24] Du X, Xiao Y, Guizani M. An effective key management scheme for heterogeneous sensor networks. Ad Hoc Networks, 2007, 5: 24-34 CrossRef Google Scholar

[25] Fahad A, Alshatri N, Tari Z. A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis. IEEE Trans Emerg Top Comput, 2014, 2: 267-279 CrossRef Google Scholar

[26] Zhang C, Zhu L, Xu C. PPDP: An efficient and privacy-preserving disease prediction scheme in cloud-based e-Healthcare system. Future Generation Comput Syst, 2018, 79: 16-25 CrossRef Google Scholar

[27] Guan Z, Si G, Zhang X. Privacy-Preserving and Efficient Aggregation Based on Blockchain for Power Grid Communications in Smart Communities. IEEE Commun Mag, 2018, 56: 82-88 CrossRef Google Scholar

[28] Guan Z, Zhang Y, Zhu L. EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid. Sci China Inf Sci, 2019, 62: 32103 CrossRef Google Scholar

[29] Shen M, Ma B, Zhu L. Secure Phrase Search for Intelligent Processing of Encrypted Data in Cloud-Based IoT. IEEE Internet Things J, 2019, 6: 1998-2008 CrossRef Google Scholar

[30] Shen M, Ma B, Zhu L. Cloud-Based Approximate Constrained Shortest Distance Queries Over Encrypted Graphs With Privacy Protection. IEEE TransInformForensic Secur, 2018, 13: 940-953 CrossRef 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. 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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    Figure 1

    Bitcoin transaction. (a) Bitcoin transaction diagram; (b) multi-input transaction diagram; (c) coinbase transaction diagram; (d) change address diagram.

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    Figure 2

    (Color online) System model of transaction analysis.

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    Figure 3

    (Color online) Address transaction relationship diagram.

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    Figure 4

    (Color online) The user incidence relation diagram (different colored addresses belonging to different subcommunities).

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    Figure 5

    (Color online) Different data group results.

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    Figure 6

    (Color online) Different iteration results.

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    Figure 7

    (Color online) Different heuristic method results.

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    Figure 8

    (Color online) Runtime comparison between the Louvain and improved Louvain algorithms.

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    Figure 9

    (Color online) Increase of $\triangle~Q$ with the improved Louvain algorithm.

  •   

    Algorithm 1 Scrapy web spider

    Require:User URL seeds $S=\{s_{1},~s_{2},~\ldots,~s_{n}\}$, $k\leq~n$;

    Output:Bitcoin addresses.

    Initialize $S=\{s_{1},~s_{2},~\ldots,~s_{n}\}$;

    for $k~\in~[1,n]$

    Acquire $s_{k}$ page data;

    if page data contains the addresses then

    Store the addresses;

    end if

    Find the associated URL from the user's URL;

    Save URL of page to $S$;

    end for

    return Addresses.

  •   

    Algorithm 2 Confirmation of the bitcoin address incidence relation

    Require:Inquiry addresses;

    Output:Addresses in the same cluster.

    Initialize address set $A=\{a_{1},~a_{2},~\ldots,~a_{n}\}$, $k\leq~n$;

    Initialize transaction set $T=\{t_{1},~t_{2},~\ldots,~t_{n}\}$;

    Acquire data of the address transaction to save to $A$;

    for $k~\in~[1,n]$

    Acquire $s_{k}$ page data;

    if $t$ is coinbase transaction then

    Addresses outputted to save to $A$;ELSIFone of Output$(t)$ belongs to a known mining pool and number of Output$(t)~\geq~100$

    Addresses outputted to save to $A$;

    else

    Addresses inputted to save to $A$;

    Judge change address to save to $A$;

    end if

    end for

    return Addresses.

  •   

    Algorithm 3 Clustering of the historical transaction data

    Require:Historical transaction data;

    Output:Addresses in the same cluster.

    Initialize transaction data set txLsit;

    Initialize a mapping set of address and cluster labels ${\rm~addr}\_{\rm~label}$;

    Initialize temporary address set tempList and temporary label ${\rm~tempLabel}=0$;

    Take the transaction ${\rm~TX}_{n}$ out of txList;

    for $k~\in~[1,n]$ and ${\rm~tempLabel}=0$

    if ${\rm~TX}_{n}$ is coinbase transaction then

    Extract all output addresses from ${\rm~TX}_{n}$ and add them to the collection tempList;

    end if

    for $k~\in~[1,n]$

    Take an undetected address from tempList;

    if the same address can be found in tempList then

    The label corresponding to this address is written to tempLabel;

    else

    The address is written back to tempList;

    end if

    end for

    Write all the addresses in tempList to ${\rm~addr}\_{\rm~label}$ and assign the same label to those addresses;

    ${\rm~addr}\_{\rm~label}$+;

    end for

    return Addresses.

  •   

    Algorithm 4 $G=(V,~E)$ pretreatment

    Require:$G=(V,~E)$ using adjacency list;

    Output:The result of division of $G~(V,~E)$.

    Initialize $G$;

    Calculate the module value $Q_{1},~Q_{0}~=~Q_{1}$;

    $Q_{2}~=~Q_{1}$;

    for $k~\in~[1,N]$

    if $V_{i}$ = 1 then

    Put vertex $V_{i}$ to the community connected with it;

    else

    Take vertex $V_{i}$ out of original community;

    Put $V_{i}$ to community with largest $\Delta~Q$;

    end if

    end for

    Calculate the module value $Q_{1}$;

    if $Q_{2}>Q_{1}$ then

    Go to step 2;

    end if

    Combine the various communities into one hyperdot.

  •   

    Algorithm 5 $G=(V,~E)$ coarseness inverse operation optimization

    Require:$G=(V,~E)$;

    Output:The result of division of $G~(V,~E)$.

    Initialize $G$;

    Check node $V_{i}$ of every community connecting outside;

    Check node $V_{j}$ with highest degree in every community;

    Adopt K-medoids algorithm;

    Calculate distance value $D_{ij}$ of every $V_{i}$ and every $V_{j}$;

    Calculate minimum value of $D_{ij}$;

    $V_{i}$ save to community of $V_{j}$.