{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:22:23Z","timestamp":1780384943582,"version":"3.54.1"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Agency of Science, Technology and Research (A*STAR) through the RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Programme","award":["A19E3b0099"],"award-info":[{"award-number":["A19E3b0099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tifs.2021.3090959","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T19:53:21Z","timestamp":1624305201000},"page":"3740-3752","source":"Crossref","is-referenced-by-count":64,"title":["DOReN: Toward Efficient Deep Convolutional Neural Networks with Fully Homomorphic Encryption"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7197-8028","authenticated-orcid":false,"given":"Souhail","family":"Meftah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8629-9052","authenticated-orcid":false,"given":"Benjamin Hong Meng","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3593-8790","authenticated-orcid":false,"given":"Chan Fook","family":"Mun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5652-3455","authenticated-orcid":false,"given":"Khin Mi Mi","family":"Aung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bharadwaj","family":"Veeravalli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Chandrasekhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Privacy-preserving classification on deep neural network","author":"chabanne","year":"2017"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3310273.3323047"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1515\/jmc-2015-0016"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.2967740"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59013-0_27"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-46800-5_25"},{"key":"ref37","first-page":"770","article-title":"Deep residual learning for image recognition","author":"he","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref36","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"arXiv 1409 1556"},{"key":"ref35","author":"authors","year":"2020","journal-title":"Tfhe Library"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56614-6_4"},{"key":"ref10","article-title":"BinaryNet: Training deep neural networks with weights and activations constrained to +1 or ?1","author":"courbariaux","year":"2016","journal-title":"arXiv 1602 02830 [cs]"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53887-6_1"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090262"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-48051-9_11"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2018.2816656"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10623-012-9720-4"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243837"},{"key":"ref18","first-page":"812","article-title":"Low latency privacy preserving inference","volume":"97","author":"brutzkus","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn (ICML)"},{"key":"ref19","article-title":"CaRENets: Compact and resource-efficient CNN for homomorphic inference on encrypted medical images","author":"chao","year":"2019","journal-title":"arXiv 1901 10074"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref4","first-page":"122","article-title":"Policy based context aware service level agreement (SLA) management in the cloud","author":"chraibi","year":"2017","journal-title":"Proc CLOUD Comput 8th Int Conf Cloud Comput GRIDs Virtualization"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44371-2_31"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.06.017"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2018.06.001"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-78381-9_12"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISVLSI.2015.66"},{"key":"ref8","first-page":"201","article-title":"CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy","volume":"48","author":"gilad-bachrach","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.12785\/ijcds\/080505"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOMW.2014.7063384"},{"key":"ref9","first-page":"483","article-title":"Fast homomorphic evaluation of deep discretized neural networks","author":"bourse","year":"2017","journal-title":"Adv Cryptology"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2013.6657078"},{"key":"ref20","first-page":"10035","article-title":"SHE: A fast and accurate deep neural network for encrypted data","author":"lou","year":"2019","journal-title":"Proc Annu Conf Neural Inf Process Syst NeurIPS"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70694-8_14"},{"key":"ref21","article-title":"Incremental network quantization: Towards lossless CNNs with low-precision weights","author":"zhou","year":"2017","journal-title":"arXiv 1702 03044"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32009-5_50"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2020.3014636"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29011-4_28"},{"key":"ref25","article-title":"Somewhat practical fully homomorphic encryption","author":"fan","year":"2012"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9151439\/09460962.pdf?arnumber=9460962","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:52:40Z","timestamp":1652194360000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9460962\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/tifs.2021.3090959","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}