ImpSuic: A quality updating rule in mixing coins with maximum utilities

International Journal of Intelligent SystemsVolume 36, Issue 3 p. 1182-1198 RESEARCH ARTICLE ImpSuic: A quality updating rule in mixing coins with maximum utilities Xinying Yu, orcid.org/0000-0001-8891-6723 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorZhaojie Wang, orcid.org/0000-0002-9483-8204 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYilei Wang, Corresponding Author ylwangqfnu@163.com orcid.org/0000-0002-1602-3720 School of Computer Science, Qufu Normal University, Rizhao, China Correspondence Yilei Wang, School of Computer Science, Qufu Normal University, Rizhao 276826, China. Email: ylwangqfnu@163.comSearch for more papers by this authorFengyin Li, orcid.org/0000-0002-5730-3315 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorTao Li, orcid.org/0000-0002-1448-3619 School of Computer Science, Qufu Normal University, Rizhao, China College of Computer Science and Technology, Guizhou University, Guiyang, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ChinaSearch for more papers by this authorYuling Chen, orcid.org/0000-0002-8674-8356 College of Computer Science and Technology, Guizhou University, Guiyang, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ChinaSearch for more papers by this authorYouliang Tian, orcid.org/0000-0002-5974-1570 College of Computer Science and Technology, Guizhou University, Guiyang, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ChinaSearch for more papers by this authorXiaomei Yu, orcid.org/0000-0002-8342-6714 School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this author Xinying Yu, orcid.org/0000-0001-8891-6723 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorZhaojie Wang, orcid.org/0000-0002-9483-8204 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYilei Wang, Corresponding Author ylwangqfnu@163.com orcid.org/0000-0002-1602-3720 School of Computer Science, Qufu Normal University, Rizhao, China Correspondence Yilei Wang, School of Computer Science, Qufu Normal University, Rizhao 276826, China. Email: ylwangqfnu@163.comSearch for more papers by this authorFengyin Li, orcid.org/0000-0002-5730-3315 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorTao Li, orcid.org/0000-0002-1448-3619 School of Computer Science, Qufu Normal University, Rizhao, China College of Computer Science and Technology, Guizhou University, Guiyang, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ChinaSearch for more papers by this authorYuling Chen, orcid.org/0000-0002-8674-8356 College of Computer Science and Technology, Guizhou University, Guiyang, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ChinaSearch for more papers by this authorYouliang Tian, orcid.org/0000-0002-5974-1570 College of Computer Science and Technology, Guizhou University, Guiyang, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, ChinaSearch for more papers by this authorXiaomei Yu, orcid.org/0000-0002-8342-6714 School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this author First published: 11 December 2020 https://doi.org/10.1002/int.22337Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder. Volume36, Issue3March 2021Pages 1182-1198 RelatedInformation