{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:32:54Z","timestamp":1760016774000,"version":"3.37.3"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1636211","61170189"],"award-info":[{"award-number":["U1636211","61170189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2016QY04W0802"],"award-info":[{"award-number":["2016QY04W0802"]}]},{"DOI":"10.13039\/501100011347","name":"State Key Laboratory of Software Development Environment","doi-asserted-by":"publisher","award":["SKLSDE-2019ZX-17"],"award-info":[{"award-number":["SKLSDE-2019ZX-17"]}],"id":[{"id":"10.13039\/501100011347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2019.2961126","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T04:56:55Z","timestamp":1577163415000},"page":"789-801","source":"Crossref","is-referenced-by-count":5,"title":["Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6185-8201","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5498-3474","authenticated-orcid":false,"given":"Liqun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9603-9713","authenticated-orcid":false,"given":"Zhoujun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-004-0148-7"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622071"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23780-5_18"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2003.1250922"},{"article-title":"Semi-supervised learning with graphs","year":"2005","author":"zhu","key":"ref31"},{"key":"ref30","first-page":"912","article-title":"Semi-supervised learning using Gaussian fields and harmonic functions","author":"zhu","year":"2003","journal-title":"Proc 20th Int Conf Mach Learn (ICML)"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2013.131"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559862"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2597444"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s12083-015-0356-9"},{"key":"ref10","first-page":"154","article-title":"Efficient privacy preserving k-means clustering","author":"upmanyu","year":"2010","journal-title":"Proc IEEE Int Workshop"},{"key":"ref40","first-page":"255","article-title":"Privacy preserving expectation maximization (EM) clustering construction","author":"hamidi","year":"2018","journal-title":"Proc Symp Distrib Comput Artif Intell"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2010.109"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref13","first-page":"217","article-title":"Privacy preserving approach for association rule mining in horizontally partitioned data using MFI and Shamir&#x2019;s secret sharing","author":"domadiya","year":"2018","journal-title":"Proc IEEE 13th Int Conf Ind Inf Syst (ICIIS)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/DSC.2018.00067"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/978-1-4020-2783-3_7","article-title":"The role of unlabeled data in supervised learning","author":"mitchell","year":"2004","journal-title":"The LOOM Knowledge Representation Language"},{"key":"ref17","first-page":"60","article-title":"Multi-view discriminative sequential learning","author":"brefeld","year":"2005","journal-title":"Proc Eur Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143863"},{"key":"ref19","first-page":"200","article-title":"Transductive inference for text classification using support vector machines","volume":"99","author":"joachims","year":"1999","journal-title":"Proc 16th Int Conf Mach Learn"},{"key":"ref28","first-page":"1","article-title":"Person identification in Webcam images: An application of semi-supervised learning","author":"balcan","year":"2005","journal-title":"Proc 22nd Int Conf Mach Learn (ICML) Workshop Learn Partially Classified Training Data"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/TKDE.2006.14","article-title":"Random projection-based multiplicative data perturbation for privacy preserving distributed data mining","volume":"18","author":"liu","year":"2006","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.03.009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/375551.375602"},{"key":"ref6","first-page":"339","article-title":"Privacy preserving data mining (PPDM) method for horizontally partitioned data","volume":"9","author":"ouda","year":"2012","journal-title":"Int J Comput Sci Issues"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2005.1570109"},{"key":"ref5","first-page":"901","article-title":"On k-anonymity and the curse of dimensionality","author":"aggarwal","year":"2005","journal-title":"Proc 32nd Int Conf Very Large Data Bases VLDB Endowment"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/978-3-319-06608-0_50","article-title":"A fast secure dot product protocol with application to privacy preserving association rule mining","author":"dong","year":"2014","journal-title":"Proc Pacific&#x2013;Asia Conf Knowl Discovery Data Mining"},{"article-title":"Privacy-preserving data mining in malicious model","year":"2006","author":"kantarcioglu","key":"ref7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014153"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2894682"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335438"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007692713085"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/978-3-540-45167-9_12","article-title":"Kernels and regularization on graphs","author":"smola","year":"2003","journal-title":"Learning Theory and Kernel Machines"},{"key":"ref21","first-page":"67","article-title":"Combining graph Laplacians for semi&#x2013;supervised learning","author":"argyriou","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5208"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6540-7"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5421-1_7"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-011-0389-1"},{"key":"ref44","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2011.156"},{"journal-title":"UCI Machine Learning Repository","year":"2017","author":"dua","key":"ref43"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1854776.1854828"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/08937544.pdf?arnumber=8937544","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T08:46:21Z","timestamp":1643273181000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8937544\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2961126","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}