{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:24:38Z","timestamp":1772645078122,"version":"3.50.1"},"reference-count":51,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892789","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":5,"title":["Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation"],"prefix":"10.1109","author":[{"given":"Bishwas","family":"Mandal","sequence":"first","affiliation":[{"name":"Kansas State University,Department of Computer Science,Manhattan,KS,USA,66506"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Amariucai","sequence":"additional","affiliation":[{"name":"Kansas State University,Department of Computer Science,Manhattan,KS,USA,66506"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangqing","family":"Wei","sequence":"additional","affiliation":[{"name":"Louisiana State University,Division of Electrical & Computer Engineering,Baton Rouge,LA,USA,70803"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2017.8007053"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2017.8262832"},{"key":"ref33","author":"xiao","year":"2017","journal-title":"Fashion-mnist a novel image dataset for benchmarking machine learning algorithms"},{"key":"ref32","article-title":"Mnist handwritten digit database","volume":"2","author":"lecun","year":"2010","journal-title":"ATT Labs [Online]"},{"key":"ref31","author":"grover","year":"2019","journal-title":"Uncertainty autoencoders Learning compressed representations via variational information maximization"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.174"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2015.7282766"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CWIT.2015.7255145"},{"key":"ref35","year":"0","journal-title":"Us census demographical data"},{"key":"ref34","author":"dua","year":"2017","journal-title":"UCI Machine Learning Repository"},{"key":"ref28","author":"ilanchezian","year":"2019","journal-title":"Maximal adversarial perturbations for obfuscation Hiding certain attributes while preserving rest"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0002"},{"key":"ref29","first-page":"906","article-title":"Obfuscation via information density estimation","volume":"108","author":"hsu","year":"2020","journal-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"dwork","year":"2014","journal-title":"Found Trends Theor Comput Sci"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref20","author":"erdemir","year":"2021","journal-title":"Active privacy-utility trade-off against a hypothesis testing adversary"},{"key":"ref22","author":"kingma","year":"2014","journal-title":"Auto-encoding variational bayes"},{"key":"ref21","author":"wu","year":"2021","journal-title":"Privacy-preserving deep action recognition An adversarial learning framework and a new dataset"},{"key":"ref24","author":"ganin","year":"2015","journal-title":"Unsupervised Domain Adaptation by Backpropagation"},{"key":"ref23","author":"goodfellow","year":"2014","journal-title":"Generative adversarial networks"},{"key":"ref26","author":"chen","year":"2018","journal-title":"VGAN-based image representation learning for privacy-preserving facial expression recognition"},{"key":"ref25","first-page":"1881","article-title":"K-beam minimax: Efficient optimization for deep adversarial learning","volume":"80","author":"hamm","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning"},{"key":"ref50","first-page":"139","article-title":"Vicus: A noise addition technique for categorical data","author":"giggins","year":"2012","journal-title":"Proceedings of the Tenth Australasian Data Mining Conference - Volume 134"},{"key":"ref51","author":"ma","year":"2020","journal-title":"Vaem a deep generative model for heterogeneous mixed type data"},{"key":"ref10","author":"edwards","year":"2016","journal-title":"Censoring representations with an adversary"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/e19120656"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2016.7888175"},{"key":"ref12","author":"madras","year":"2018","journal-title":"Learning adversarially fair and transferable representations"},{"key":"ref13","author":"pittaluga","year":"2018","journal-title":"Learning privacy preserving encodings through adversarial training"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2878698"},{"key":"ref15","author":"huang","year":"2019","journal-title":"Generative adversarial privacy"},{"key":"ref16","author":"chen","year":"2019","journal-title":"Distributed generation of privacy preserving data with user customization"},{"key":"ref17","author":"wu","year":"2020","journal-title":"Towards privacy-preserving visual recognition via adversarial training A pilot study"},{"key":"ref18","author":"morales","year":"2020","journal-title":"SensitiveNets Learning Agnostic Representations with Application to Face Images"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6930"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SmartGridComm.2011.6102315"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton.2013.6736724"},{"key":"ref5","first-page":"39","article-title":"Differential privacy: on the trade-off between utility and information leakage","author":"alvim","year":"2011","journal-title":"International Workshop on Formal Aspects in Security and Trust"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500410"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2013.2253320"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ARES.2008.97"},{"key":"ref46","article-title":"beta-vae: Learning basic visual concepts with a constrained variational framework","author":"higgins","year":"0","journal-title":"ICLRE"},{"key":"ref45","article-title":"Application-driven privacy-preserving data publishing with correlated attributes","author":"rezaei","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref48","article-title":"Learning controllable fair representations","volume":"abs 1812 4218","author":"song","year":"2018","journal-title":"CoRR"},{"key":"ref47","author":"louizos","year":"2017","journal-title":"The variational fair autoencoder"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2903658"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2018.8437735"},{"key":"ref44","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"de boer","year":"2004","journal-title":"Annals of Operations Research"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2939472"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","location":"Padua, Italy","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892789.pdf?arnumber=9892789","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T20:52:24Z","timestamp":1665780744000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892789\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892789","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}