{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:10:36Z","timestamp":1740100236862,"version":"3.37.3"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61572278"],"award-info":[{"award-number":["61572278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533885","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Privacy-Preserving Convex Factorization Machine"],"prefix":"10.1109","author":[{"given":"Jie","family":"Sun","sequence":"first","affiliation":[]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref32","article-title":"Online Convex Programming and Generalized Infinitesimal Gradient Ascent","author":"zinkevich","year":"0","journal-title":"Proceedings Twentieth International Conference on Machine Learning"},{"key":"ref31","article-title":"Implicit online learning","author":"kulis","year":"0","journal-title":"ICML 2010 - Proceedings 27th International Conference on Machine Learning"},{"key":"ref30","first-page":"1","article-title":"Differentially private online learning","author":"jain","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref10","first-page":"1633","author":"lin","year":"2018","journal-title":"Online Compact Convexified Factorization Machine"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2016.2631533"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2897845.2897875"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref14","first-page":"289","article-title":"Privacy-preserving logistic regression","author":"chaudhuri","year":"2008","journal-title":"Advances in neural information processing systems"},{"key":"ref15","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"chaudhuri","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref17","first-page":"1","article-title":"Private convex empirical risk minimization and high-dimensional regression","volume":"23","author":"kifer","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref18","first-page":"728","article-title":"(Near) dimension independent risk bounds for differentially private learning","volume":"1","author":"jain","year":"0","journal-title":"31st International Conference on Machine Learning ICML 2014"},{"key":"ref19","first-page":"263","author":"mironov","year":"0","journal-title":"Rényi Differential Privacy Proceedings - IEEE Computer Security Foundations Symposium"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.56"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2433396.2433467"},{"key":"ref27","first-page":"427","article-title":"Revisiting Frank-Wolfe: Projection-free sparse convex optimization","author":"jaggi","year":"0","journal-title":"30th International Conference on Machine Learning ICML 2013"},{"journal-title":"Field-aware Factorization Machines in a Real-world Online Advertising System[J]","year":"2017","author":"juan","key":"ref3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064047"},{"key":"ref5","article-title":"Higher-order factorization machines","author":"blondel","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23525-7_2"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013804"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098103"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/1866739.1866758"},{"key":"ref22","article-title":"Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences","author":"balle","year":"2018","journal-title":"(NeurIPS)"},{"key":"ref21","first-page":"1","article-title":"Subsampled rényi differential privacy and analytical moments accountant","author":"wang","year":"2018","journal-title":"ArXiv"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350253"},{"key":"ref23","first-page":"13169","article-title":"Poisson subsampled Renyi differential privacy","author":"zhu","year":"2019","journal-title":"36th International Conference on Machine Learning ICML 2019"},{"key":"ref26","article-title":"A probabilistic analysis of EM for mixtures of separated, spherical Gaussians","author":"dasgupta","year":"2007","journal-title":"Journal of Machine Learning Research"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01187"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533885.pdf?arnumber=9533885","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:46:14Z","timestamp":1652197574000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533885\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533885","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}