{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:08:04Z","timestamp":1730200084742,"version":"3.28.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"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":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006313","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T01:05:34Z","timestamp":1582592734000},"page":"5728-5736","source":"Crossref","is-referenced-by-count":1,"title":["Deep Neural Networks as Similitude Models for Sharing Big Data"],"prefix":"10.1109","author":[{"given":"Philip","family":"Derbeko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shlomi","family":"Dolev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehud","family":"Gudes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Auto-encoding variational bayes","volume":"abs 1312 6114","author":"kingma","year":"2014","journal-title":"CoRR"},{"key":"ref38","first-page":"1","article-title":"Learning deep architectures for ai","volume":"2","author":"yoshua","year":"2009","journal-title":"Foundations"},{"journal-title":"Theory of Probability (Russian)","year":"1927","author":"bernstein","key":"ref33"},{"key":"ref32","article-title":"Adversarial autoencoders","volume":"abs 1511 5644","author":"makhzani","year":"2015","journal-title":"CoRR"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2001.989568"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/800057.808710"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1417"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1214\/aos\/1176342611","article-title":"Probability inequalities for the sum in sampling without replacement","volume":"2","author":"serfling","year":"1974","journal-title":"Ann Statist"},{"journal-title":"Probability Inequalities for Sums of Bounded Random Variables","year":"1962","author":"hoeffding","key":"ref34"},{"journal-title":"Clustering and the Continuous K-means Algorithm","year":"1994","author":"faber","key":"ref10"},{"key":"ref40","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"NIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"journal-title":"Principal Component Analysis Ser Springer Series in Statistics","year":"2002","author":"jolliffe","key":"ref12"},{"key":"ref13","first-page":"1","article-title":"Differential privacy","author":"dwork","year":"2006","journal-title":"ICALP"},{"key":"ref14","first-page":"265","article-title":"Calibrating noise to sensitivity in private data analysis","volume":"2006","author":"dwork","year":"2006","journal-title":"Theory of Cryptography Third Theory of Cryptography Conference TCC 2006"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1250790.1250803"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2010.5447831"},{"key":"ref18","article-title":"Security and privacy issues in deep learning","volume":"abs 1807 11655","author":"bae","year":"2018","journal-title":"CoRR"},{"key":"ref19","first-page":"160","article-title":"Protocols for secure computations","volume":"0","author":"yao","year":"1982","journal-title":"2013 IEEE 54th Annual Symposium on Foundations of Computer Science"},{"key":"ref28","article-title":"Denoising adversarial autoencoders","author":"creswell","year":"2018","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref27","article-title":"Generalized denoising auto-encoders as generative models","author":"bengio","year":"2013","journal-title":"NIPS"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-61638-9"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.80"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref5","article-title":"Differentially private generative adversarial networks for time series, continuous, and discrete open data","author":"frigerio","year":"2019","journal-title":"Sec"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1055558.1055582"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807247"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94147-9_9"},{"journal-title":"Wavelets for Computer Graphics Theory and Applications","year":"1996","author":"stollnitz","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258489"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/276305.276344"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/375551.375598"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s007780100049"},{"key":"ref42","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"AISTATS"},{"key":"ref24","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"NIPS"},{"key":"ref41","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"NAACL"},{"key":"ref23","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"2014","journal-title":"arXiv preprint arXiv 1401 4082"},{"key":"ref44","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref26","article-title":"Continual learning in generative adversarial nets","volume":"abs 1705 8395","author":"seff","year":"2017","journal-title":"CoRR"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref25","article-title":"Deep generative stochastic networks trainable by backprop","author":"bengio","year":"2014","journal-title":"ICML"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2019,12,9]]},"location":"Los Angeles, CA, USA","end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006313.pdf?arnumber=9006313","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T17:52:00Z","timestamp":1658080320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006313\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006313","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}