{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:46:50Z","timestamp":1742975210287,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":10,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811982842"},{"type":"electronic","value":"9789811982859"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":343,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Traditional anti-anonymity technologies for Bitcoin transactions include two types. One is network-layer anti-anonymity technology, which achieves the purpose of locating the initial IP of specific transaction information by speculating on the IP propagation path of transaction; the other is the anti-anonymity technology of the transaction layer. By analyzing the data of the Bitcoin ledger, it realizes the on-chain behavior portrait of a specific wallet address attributable to the user. In this work, we propose a new anti-anonymity technology, by constructing transaction behavior vectors and social behavior vectors based on Bitcoin ledger data and off-chain social data respectively, and build a model for mapping and aligning the two vectors. Experimental test shows that the proposed anti-anonymity technology is more accurate and has better practical effects. Furthermore, the technology suits for the anti-anonymity of other virtual currencies as well.<\/jats:p>","DOI":"10.1007\/978-981-19-8285-9_10","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T20:02:48Z","timestamp":1670616168000},"page":"139-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Bitcoin Anti-anonymity Technology Based on Behavior Vectors Mapping and Aligning Model"],"prefix":"10.1007","author":[{"given":"Shenwen","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinglin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"10_CR1","unstructured":"Nakamoto S.: Bitcoin: A peer-to-peer electronic cash system [EB\/OL]. https:\/\/www.Bitcoin.org\/Bitcoin.pdf,last. Accessed 18 May 2022"},{"issue":"2","key":"10_CR2","first-page":"27","volume":"41","author":"MA Hong-liang","year":"2018","unstructured":"Hong-liang, M.A., Zhen, W.U., Min, H.E., Ji-qiang, T.A., Meng, S.H.: Heuristic approaches based clustering of bitcoin addresses. J. Beijing Univ. Posts Telecom 41(2), 27\u201331 (2018)","journal-title":"J. Beijing Univ. Posts Telecom"},{"issue":"5","key":"10_CR3","first-page":"899","volume":"41","author":"F Gao","year":"2018","unstructured":"Gao, F., Mao, H.L., Wu, Z., Shen, M., Zhu, L., Li, Y.D.: Lightweight transaction tracing technology for bitcoin. Chin. J. Comput. 41(5), 899\u20131004 (2018)","journal-title":"Chin. J. Comput."},{"key":"10_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-662-45472-5_30","volume-title":"Financial Cryptography and Data Security","author":"P Koshy","year":"2014","unstructured":"Koshy, P., Koshy, D., McDaniel, P.: An analysis of anonymity in Bitcoin using P2P network traffic. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 469\u2013485. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45472-5_30"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Biryukov, A., Khovratovich, D., Pustogarov, I.: Deanonymisation of clients in Bitcoin P2P network. In: Proceedings of the 21st ACM Conference on Computer and Communications Security, pp. 15\u201329. New York, USA (2014)","DOI":"10.1145\/2660267.2660379"},{"key":"10_CR6","unstructured":"Ivan, P.: Deanonymisation Techniques for Tor and Bitcoin [Ph.D. dissertation]. University of Luxem-bourg, Luxem bourg (2015)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Liao, K., Zhao, Z., Doupe, A., et al.: Behind closed doors measurement and analysis of crypto locker ransoms in Bitcoin. In: Proceedings of the Symposium on Electronic Crime Research. Toronto, Canada, pp. 1\u201313 (2016)","DOI":"10.1109\/ECRIME.2016.7487938"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Meiklejohn, S., Pomarole, M., Jordan, G., et al.: A fistful of Bitcoins: characterizing payments among men with no names. In: Proceedings of the Conference on Internet Measurement, pp. 127\u2013140. Barcelona, Spain (2013)","DOI":"10.1145\/2504730.2504747"},{"issue":"12","key":"10_CR9","first-page":"47","volume":"47","author":"W Guo","year":"2021","unstructured":"Guo, W., Yang, X., Feng, Z., Zhang, L., Yang, J.: Research of de-anonymizing method based on machine learning for bitcoin. Comput. Eng. 47(12), 47\u201353 (2021)","journal-title":"Comput. Eng."},{"key":"10_CR10","unstructured":"Jawaheri, H.A., Sabah, M.A., Boshmaf, Y., et al.: When a small leak sinks a great ship: deanonymizing tor hidden service users through Bitcoin transactions analysis[EB\/OL], https:\/\/arxiv.org\/pdf\/1801.07501.pdf,last. 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