{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T09:49:10Z","timestamp":1784454550967,"version":"3.55.0"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB2104003"],"award-info":[{"award-number":["2020YFB2104003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1109\/tetci.2022.3182415","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T19:49:48Z","timestamp":1657568988000},"page":"1427-1437","source":"Crossref","is-referenced-by-count":6,"title":["Taking Away Both Model and Data: Remember Training Data by Parameter Combinations"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8357-1655","authenticated-orcid":false,"given":"Wenjian","family":"Luo","sequence":"first","affiliation":[{"name":"School of Harbin Institute of Technology, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Licai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Harbin Institute of Technology, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3845-7749","authenticated-orcid":false,"given":"Peiyi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Harbin Institute of Technology, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Harbin Institute of Technology, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongfei","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Harbin Institute of Technology, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref38","article-title":"Algorithms for manifold learning","author":"cayton","year":"2005"},{"key":"ref33","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref32","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2011.37"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2018.2881153"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3059661"},{"key":"ref35","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"},{"key":"ref34","first-page":"318","article-title":"Learning internal representations by error propagation","volume":"i","author":"rumelhart","year":"1986","journal-title":"Parallel Distributed Processing"},{"key":"ref10","article-title":"Privacy inference attacks and defenses in cloud-based deep neural network: A survey","author":"zhang","year":"2021"},{"key":"ref11","article-title":"Adversarial neural network inversion via auxiliary knowledge alignment","author":"yang","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484533"},{"key":"ref13","first-page":"17","article-title":"Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing","author":"fredrikson","year":"0","journal-title":"Proc 23rd USENIX Secur Symp Secur"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01587"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0008"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489592"},{"key":"ref19","first-page":"601","article-title":"Stealing machine learning models via prediction APIS","author":"tram\u00e8r","year":"0","journal-title":"Proc 25th USENIX Secur Symp"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3009876"},{"key":"ref27","first-page":"3","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"dwork","year":"2014","journal-title":"Found Trends Theor Comput Sci"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICITST.2015.7412089"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2006"},{"key":"ref29","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"mart\u00edn","year":"0","journal-title":"Proc 12th USENIX Symp Operating Syst Des Implementation"},{"key":"ref5","first-page":"5558","article-title":"White-box vs black-box: Bayes optimal strategies for membership inference","author":"sablayrolles","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134077"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa8415"},{"key":"ref1","first-page":"98","article-title":"Machine learning basics","volume":"1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref9","first-page":"175","article-title":"Bypassing backdoor detection algorithms in deep learning","author":"shokri","year":"0","journal-title":"Proc IEEE Symp Privacy Secur"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00038"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243834"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274696"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1504\/IJSN.2015.071829"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909068"},{"key":"ref25","article-title":"BadNets: Identifying vulnerabilities in the machine learning model supply chain","author":"gu","year":"2017"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7433297\/9965775\/09826434.pdf?arnumber=9826434","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T19:42:50Z","timestamp":1671478970000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9826434\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":38,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2022.3182415","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12]]}}}