{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T04:23:05Z","timestamp":1778127785897,"version":"3.51.4"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100014553","name":"Samsung Advanced Institute of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014553","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3159694","type":"journal-article","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T21:17:06Z","timestamp":1647292626000},"page":"30039-30054","source":"Crossref","is-referenced-by-count":329,"title":["Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network"],"prefix":"10.1109","volume":"10","author":[{"given":"Joon-Woo","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5566-5358","authenticated-orcid":false,"given":"Hyungchul","family":"Kang","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9424-6498","authenticated-orcid":false,"given":"Yongwoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Suwon, Republic of Korea"}]},{"given":"Woosuk","family":"Choi","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Suwon, Republic of Korea"}]},{"given":"Jieun","family":"Eom","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6761-3667","authenticated-orcid":false,"given":"Maxim","family":"Deryabin","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5270-2405","authenticated-orcid":false,"given":"Eunsang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9476-3313","authenticated-orcid":false,"given":"Junghyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, Republic of Korea"}]},{"given":"Donghoon","family":"Yoo","sequence":"additional","affiliation":[{"name":"Samsung Advanced Institute of Technology, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4114-4935","authenticated-orcid":false,"given":"Young-Sik","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Chosun University, Gwangju, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3946-0958","authenticated-orcid":false,"given":"Jong-Seon","family":"No","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, Republic of Korea"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"SHE: A fast and accurate deep neural network for encrypted data","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Lou"},{"key":"ref2","article-title":"Somewhat practical fully homomorphic encryption","author":"Fan","year":"2020"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10970-7_16"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-78381-9_14"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17656-3_2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-40186-3_16"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-77870-5_21"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-77870-5_22"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2021.i4.114-148"},{"key":"ref12","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref13","volume-title":"Microsoft SEAL","year":"2021"},{"key":"ref14","article-title":"Precise approximation of convolutional neural networks for homomorphically encrypted data","author":"Lee","year":"2021","journal-title":"arXiv:2105.10879"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3407023.3407045"},{"key":"ref16","first-page":"201","article-title":"Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Gilad-Bachrach"},{"key":"ref17","article-title":"Faster CryptoNets: Leveraging sparsity for real-world encrypted inference","author":"Chou","year":"2018","journal-title":"arXiv:1811.09953"},{"key":"ref18","article-title":"SEALion: A framework for neural network inference on encrypted data","author":"van Elsloo","year":"2019","journal-title":"arXiv:1904.12840"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2020.3014636"},{"key":"ref20","article-title":"CryptoDL: Deep neural networks over encrypted data","author":"Hesamifard","year":"2017","journal-title":"arXiv:1711.05189"},{"key":"ref21","first-page":"1651","article-title":"GAZELLE: A low latency framework for secure neural network inference","volume-title":"Proc. 27th USENIX Secur. Symp.","author":"Juvekar"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00013"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3310273.3323047"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3338469.3358944"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3014369"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3105111"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-21769-x_9"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34621-8_15"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2925425"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-95312-6_6"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-020-0719-9"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-06944-4_19"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09734024.pdf?arnumber=9734024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T23:05:23Z","timestamp":1705532723000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9734024\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3159694","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}