{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T06:10:32Z","timestamp":1784873432910,"version":"3.55.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council through the DECRA project","doi-asserted-by":"publisher","award":["DE200100166"],"award-info":[{"award-number":["DE200100166"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tifs.2023.3274391","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T18:40:27Z","timestamp":1683571227000},"page":"3088-3100","source":"Crossref","is-referenced-by-count":54,"title":["Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3022-2205","authenticated-orcid":false,"given":"Ngoc Duy","family":"Pham","sequence":"first","affiliation":[{"name":"School of Computing, Engineering, and Mathematical Sciences (SCEMS), La Trobe University, Melbourne, VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alsharif","family":"Abuadbba","sequence":"additional","affiliation":[{"name":"CSIRO&#x2019;s Data61, Eveleigh, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5783-2172","authenticated-orcid":false,"given":"Yansong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering, and Mathematical Sciences (SCEMS), La Trobe University, Melbourne, VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0471-9402","authenticated-orcid":false,"given":"Khoa Tran","family":"Phan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering, and Mathematical Sciences (SCEMS), La Trobe University, Melbourne, VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5396-8897","authenticated-orcid":false,"given":"Naveen","family":"Chilamkurti","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering, and Mathematical Sciences (SCEMS), La Trobe University, Melbourne, VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","first-page":"16937","article-title":"Inverting gradients&#x2014;How easy is it to break privacy in federated learning?","volume":"33","author":"geiping","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","first-page":"14774","author":"zhu","year":"2019","journal-title":"Deep Leakage from Gradients"},{"key":"ref15","first-page":"30","article-title":"Split computing and early exiting for deep learning applications: Survey and research challenges","volume":"55","author":"matsubara","year":"2022","journal-title":"ACM Comput Surv"},{"key":"ref14","article-title":"PPA: Preference profiling attack against federated learning","author":"zhou","year":"2022","journal-title":"arXiv 2202 04856"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref52","first-page":"7","article-title":"Reducing duplicate filters in deep neural networks","author":"roychowdhury","year":"2017","journal-title":"Proc NIPS Workshop Deep Learning Bridging Theory Pract"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220106"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080215"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2763126"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3320269.3384740"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR.2019.00068"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988156"},{"key":"ref51","first-page":"730","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc 3rd Int Conf Learn Represent (ICLR)"},{"key":"ref50","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref46","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref45","article-title":"An overview of gradient descent optimization algorithms","author":"ruder","year":"2016","journal-title":"arXiv 1609 04747"},{"key":"ref48","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"ArXiv 1708 07747"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-92659-5_7"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-022-03113-7"},{"key":"ref44","author":"near","year":"2021","journal-title":"Programming Differential Privacy"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.2307\/2283137"},{"key":"ref49","first-page":"9","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"Proc NIPS Workshop Deep Learn Unsupervised Feature Learn"},{"key":"ref8","article-title":"Privacy in deep learning: A survey","author":"mireshghallah","year":"2020","journal-title":"arXiv 2004 12254"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2020.2976185"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.041"},{"key":"ref4","year":"2016","journal-title":"General Data Protection Regulation (Regulation (EU) 2016\/679)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00019"},{"key":"ref6","article-title":"No peek: A survey of private distributed deep learning","author":"vepakomma","year":"2018","journal-title":"arXiv 1812 03288"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1005817.1005840"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref35","article-title":"Understanding straight-through estimator in training activation quantized neural nets","author":"yin","year":"2019","journal-title":"arXiv 1903 05662"},{"key":"ref34","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"bengio","year":"2013","journal-title":"arXiv 1308 3432"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref36","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00524"},{"key":"ref30","first-page":"3123","article-title":"BinaryConnect: Training deep neural networks with binary weights during propagations","volume":"2","author":"courbariaux","year":"2015","journal-title":"Proc 28th Int Conf Neural Inf Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8060661"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107281"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119326"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359824"},{"key":"ref38","article-title":"Modelling and automatically analysing privacy properties for honest-but-curious adversaries","author":"paverd","year":"2014"},{"key":"ref24","first-page":"4114","article-title":"Binarized neural networks","author":"hubara","year":"2016","journal-title":"Proc 30th Int Conf Neural Inf Process Syst"},{"key":"ref23","article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1","author":"courbariaux","year":"2016","journal-title":"arXiv 1602 02830 [cs]"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1063\/5.0066470"},{"key":"ref25","article-title":"Noiseless privacy","author":"farokhi","year":"2019","journal-title":"arXiv 1910 13027"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW51313.2020.00134"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3485259"},{"key":"ref21","first-page":"1","article-title":"Reducing leakage in distributed deep learning for sensitive health data","author":"vepakomma","year":"2019","journal-title":"Proc ICLR AI Social Good Workshop"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref29","article-title":"Detailed comparison of communication efficiency of split learning and federated learning","author":"singh","year":"2019","journal-title":"arXiv 1909 09145"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9970396\/10121474.pdf?arnumber=10121474","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T17:53:51Z","timestamp":1687197231000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10121474\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3274391","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}