{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T06:40:23Z","timestamp":1776753623148,"version":"3.51.2"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","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":[[2020,1,1]]},"DOI":"10.1109\/tkde.2018.2878698","type":"journal-article","created":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T15:28:37Z","timestamp":1540913317000},"page":"54-66","source":"Crossref","is-referenced-by-count":62,"title":["Notice of Removal: Deep Private-Feature Extraction"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7613-5292","authenticated-orcid":false,"given":"Seyed Ali","family":"Osia","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Advanced ICT Innovation Center, Sharif University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Taheri","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Advanced ICT Innovation Center, Sharif University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Shahin","family":"Shamsabadi","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2945-5434","authenticated-orcid":false,"given":"Kleomenis","family":"Katevas","sequence":"additional","affiliation":[{"name":"Dyson School of Design Engineering, Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamed","family":"Haddadi","sequence":"additional","affiliation":[{"name":"Dyson School of Design Engineering, Imperial College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9835-4493","authenticated-orcid":false,"given":"Hamid R.","family":"Rabiee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Advanced ICT Innovation Center, Sharif University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa1465"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2398776.2398812"},{"key":"ref3","article-title":"Privacy leakage in mobile computing: Tools, methods, and characteristics","author":"Haris","year":"2014"},{"key":"ref4","first-page":"1","article-title":"Quantified self and the privacy challenge","author":"Haddadi","year":"2014","journal-title":"Technol. Law Futures"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22444-7_15"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/1687-417X-2007-013801"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2831347.2831354"},{"key":"ref8","article-title":"A hybrid deep learning architecture for privacy-preserving mobile analytics","author":"Osia","year":"2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2018.2381113"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335438"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/375551.375602"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-70992-5_2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.143"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2004.10110"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.42"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2006.101"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217302"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2007.367856"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.190"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.32"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref23","article-title":"Semi-supervised knowledge transfer for deep learning from private training data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Papernot"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref28","article-title":"Differential privacy and machine learning: A survey and review","author":"Ji","year":"2014"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5555\/3241094.3241142"},{"key":"ref31","first-page":"201","article-title":"Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilad-Bachrach"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465894"},{"key":"ref33","article-title":"Gazelle: A low latency framework for secure neural network inference","author":"Juvekar","year":"2018"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2763126"},{"key":"ref35","article-title":"Deeprotect: Enabling inference-based access control on mobile sensing applications","author":"Liu","year":"2017"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/IoTDI.2018.00025"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.6.1129"},{"key":"ref38","first-page":"201","article-title":"The IM algorithm: A variational approach to information maximization","volume-title":"Proc. 16th Int. Conf. Neural Inf. Process. Syst.","author":"Barber"},{"key":"ref39","first-page":"2172","article-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref40","first-page":"368","article-title":"The information bottleneck method","volume-title":"Proc. 37th Annu. Allerton Conf. Commun. Control Comput.","author":"Tishby"},{"key":"ref41","article-title":"Deep variational information bottleneck","author":"Alemi","year":"2016"},{"key":"ref42","volume-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)","author":"Bishop","year":"2006"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2004.04.004"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1201\/9781315140919"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-49430-8_2"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"key":"ref49","article-title":"Privacy-preserving deep inference for rich user data on the cloud","author":"Osia","year":"2017"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref51","article-title":"Opening the black box of deep neural networks via information","author":"Shwartz-Ziv","year":"2017"},{"key":"ref52","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Yosinski"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.522"},{"key":"ref54","volume-title":"Elements Inf. Theory","author":"Cover","year":"2012"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_2"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref58","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref59","article-title":"Compression of deep convolutional neural networks for fast and low power mobile applications","author":"Kim","year":"2015"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/8926561\/08515092.pdf?arnumber=8515092","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T05:46:02Z","timestamp":1776750362000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8515092\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":59,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2018.2878698","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":[[2020,1,1]]}}}