Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80800
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dc.contributor.authorZhang, ZCen_US
dc.contributor.authorChung, FLen_US
dc.contributor.authorWang, STen_US
dc.date.accessioned2019-05-28T01:09:29Z-
dc.date.available2019-05-28T01:09:29Z-
dc.date.issued2019-
dc.identifier.citationAutomatika, 2019, v. 60, no. 1, p. 58-67en_US
dc.identifier.issn0005-1144en_US
dc.identifier.urihttp://hdl.handle.net/10397/80800-
dc.description.abstractWe propose a novel two-party privacy-preserving classification solution called Collaborative Classification Mechanism for Privacy-preserving ((CMP)-M-2 (2))over horizontally partitioned data that is inspired from the fact, that global and local learning can be independently executed in two parties. This model collaboratively trains the decision boundary from two hyper-planes individually constructed by its own privacy data and global data. (CMP2)-M-2 can hide true data entries and ensure the two-parties' privacy. We describe its definition and provide an algorithm to predict future data point based on Goethals's Private Scalar Product Protocol. Moreover, we show that (CMP2)-M-2 can be transformed into existing Minimax Probability Machine (MPM), Support Vector Machine (SVM) and Maxi-Min Margin Machine (M-4) model when privacy data satisfy certain conditions. We also extend (CMP2)-M-2 to a nonlinear classifier by exploiting kernel trick. Furthermore, we perform a series of evaluations on real-world benchmark data sets. Comparison with SVM from the point of protecting privacy demonstrates the advantages of our new model.en_US
dc.description.sponsorshipDepartment of Computingen_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofAutomatikaen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Zhancheng Zhang, Fu-Lai Chung & Shitong Wang (2019) Collaborative classification mechanism for privacy-Preserving on horizontally partitioned data, Automatika, 60:1,58-67 is available at https://dx.doi.org/10.1080/00051144.2019.1578039en_US
dc.subjectClassificationen_US
dc.subjectPrivacy-preservingen_US
dc.subjectCollaborative learningen_US
dc.subjectSupport vector machineen_US
dc.titleCollaborative classification mechanism for privacy-preserving on horizontally partitioned dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage58en_US
dc.identifier.epage67en_US
dc.identifier.volume60en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/00051144.2019.1578039en_US
dc.identifier.isiWOS:000460172700007-
dc.identifier.scopus2-s2.0-85065874109-
dc.identifier.eissn1848-3380en_US
dc.description.validate201905 bcrc-
dc.description.oapublished_final-
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