{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:41:54Z","timestamp":1762868514258,"version":"3.41.0"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMETRICS Perform. Eval. Rev."],"published-print":{"date-parts":[[2021,5,17]]},"abstract":"<jats:p>Bitcoin payments require a random amount of time to get confirmed (i.e. to be grouped by the miners into a block and to be added to the Bitcoin blockchain). In [8, 11], the authors propose the modelling of the Bitcoin confirmation time by the so-called time to ruin of the Cramer-Lundberg (CL) model. This provides off-the-shelf results directly aimed at predicting the confirmation time. However, analyses suggest that the data may not fully conform with the CL model assumptions. In this manuscript, we show by means of a robustness analysis that the time to ruin of a CL model is near insensitive to small changes in the model assumptions and illustrate that the proposed heuristic model can be used to accurately predict the confirmation times even when the data deviate (to a small degree) from the model assumptions.<\/jats:p>","DOI":"10.1145\/3466826.3466834","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T16:09:27Z","timestamp":1621440567000},"page":"20-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Robustness analysis of Bitcoin confirmation times"],"prefix":"10.1145","volume":"48","author":[{"given":"Ivo","family":"Stoepker","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rowel","family":"Gundlach","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stella","family":"Kapodistria","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology, Eindhoven, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-35176-2"},{"key":"e_1_2_1_2_1","first-page":"1","volume-title":"Stochastic Models","author":"Bowden R.","year":"2020","unstructured":"R. Bowden , H. P. Keeler , A. E. Krzesinski , and P. G. Taylor . Modeling and analysis of block arrival times in the bitcoin blockchain . Stochastic Models , pages 1 -- 36 , 2020 . R. Bowden, H. P. Keeler, A. E. Krzesinski, and P. G. Taylor. Modeling and analysis of block arrival times in the bitcoin blockchain. Stochastic Models, pages 1--36, 2020."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/26.4.404"},{"key":"e_1_2_1_4_1","volume-title":"Modeling and simulation of the economics of mining in the bitcoin market. PLoS One, 11(10)","author":"Cocco L.","year":"2016","unstructured":"L. Cocco and M. Marchesi . Modeling and simulation of the economics of mining in the bitcoin market. PLoS One, 11(10) , 2016 . L. Cocco and M. Marchesi. Modeling and simulation of the economics of mining in the bitcoin market. PLoS One, 11(10), 2016."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53357-4_8"},{"key":"e_1_2_1_6_1","first-page":"1","article-title":"Information propagation in the bitcoin network","volume":"2013","author":"Decker C.","year":"2013","unstructured":"C. Decker and R. Wattenhofer . Information propagation in the bitcoin network . In IEEE P2P 2013 Proceedings, pages 1 -- 10 , 2013 . C. Decker and R. Wattenhofer. Information propagation in the bitcoin network. In IEEE P2P 2013 Proceedings, pages 1--10, 2013.","journal-title":"IEEE P2P"},{"issue":"23","key":"e_1_2_1_7_1","first-page":"41","article-title":"Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay","volume":"104","author":"Gobel J.","year":"2016","unstructured":"J. Gobel , H. Keeler , A. Krzesinski , and P. Taylor . Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay . Performance Evaluation , 104 : 23 -- 41 , 2016 . 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Johoe's bitcoin mempool statistics (https:\/\/jochen-hoenicke.de\/queue) , 2020 . J. Hoenicke. Johoe's bitcoin mempool statistics (https:\/\/jochen-hoenicke.de\/queue), 2020."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0949-2"},{"key":"e_1_2_1_11_1","volume-title":"Predicting the confirmation time of bitcoin transactions","author":"Koops D.","year":"2018","unstructured":"D. Koops . Predicting the confirmation time of bitcoin transactions , 2018 . Preprint available at arXiv:1809.10596 [cs.PF]. D. Koops. Predicting the confirmation time of bitcoin transactions, 2018. 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Packages: evd, Stephenson A. G. ( 2002). R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2019. Packages: evd, Stephenson A. G. (2002)."},{"key":"e_1_2_1_17_1","volume-title":"Analysis of hashrate-based double spending","author":"Rosenfeld M.","year":"2014","unstructured":"M. Rosenfeld . Analysis of hashrate-based double spending , 2014 . Preprint available at arXiv:1402.2009v1 [cs.CR]. M. Rosenfeld. Analysis of hashrate-based double spending, 2014. 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