{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:24:31Z","timestamp":1768688671890,"version":"3.49.0"},"reference-count":39,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/tkde.2020.2997604","type":"journal-article","created":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T19:52:27Z","timestamp":1590522747000},"page":"1-1","source":"Crossref","is-referenced-by-count":23,"title":["Privacy-preserving Feature Extraction via Adversarial Training"],"prefix":"10.1109","author":[{"given":"Xiaofeng","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbiao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhilin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kim-Kwang Raymond","family":"Choo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref3","first-page":"647","article-title":"Deep learning for chinese word segmentation and pos tagging","volume-title":"Proc. Conf. Empir. Methods Natural Lang. Process.","author":"Zheng"},{"key":"ref4","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume":"1","author":"Devlin"},{"key":"ref5","first-page":"173","article-title":"Deep speech 2: End-to-end speech recognition in english and mandarin","volume-title":"Proc. 33rd Int. Conf. Int. Conf. Mach. Learn.","author":"Amodei"},{"key":"ref6","article-title":"WaveNet: A generative model for raw audio","volume-title":"Proc. ISCA Speech Synthesis Workshop","author":"Van Den Oord"},{"key":"ref7","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Sutskever"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196907"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196926"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.195"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref12","article-title":"Privacy-preserving machine learning through data obfuscation","author":"Zhang","year":"2018"},{"key":"ref13","first-page":"201","article-title":"CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy","volume-title":"Proc. 33rd Int. Conf. Int. Conf. Mach. Learn.","author":"Gilad-Bachrach"},{"key":"ref14","article-title":"CryptoDL: Deep neural networks over encrypted data","author":"Hesamifard","year":"2017"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2019.2896325"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2015.2470255"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10165"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref20","first-page":"1","article-title":"Semi-supervised knowledge transfer for deep learning from private training data","author":"Papernot"},{"key":"ref21","first-page":"1","article-title":"Scalable private learning with pate","author":"Papernot","year":"2018"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3196494.3196522"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.174"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00089"},{"key":"ref26","article-title":"PrivyNet: A flexible framework for privacy-preserving deep neural network training","author":"Li","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2878698"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220106"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_37"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-018-9512-1"},{"key":"ref32","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref33","first-page":"2234","article-title":"Improved techniques for training GANs","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231757"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.522"},{"key":"ref36","article-title":"Privacy-adversarial user representations in recommender systems","author":"Resheff","year":"2018"},{"key":"ref37","first-page":"585","article-title":"Controllable invariance through adversarial feature learning","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref39","article-title":"Understanding neural networks through deep visualization","volume-title":"Proc. Int. Conf. Mach. Learn. Workshop Deep Learn.","author":"Yosinski"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/4358933\/09099993.pdf?arnumber=9099993","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T22:36:52Z","timestamp":1704839812000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9099993\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/tkde.2020.2997604","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}