{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:56:50Z","timestamp":1774540610025,"version":"3.50.1"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"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":[[2019,7]]},"DOI":"10.1109\/isit.2019.8849381","type":"proceedings-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T21:46:12Z","timestamp":1569534372000},"page":"542-546","source":"Crossref","is-referenced-by-count":4,"title":["Synthesizing Differentially Private Datasets using Random Mixing"],"prefix":"10.1109","author":[{"given":"Kangwook","family":"Lee","sequence":"first","affiliation":[{"name":"School of EE, KAIST"}]},{"given":"Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of EE, KAIST"}]},{"given":"Kyungmin","family":"Lee","sequence":"additional","affiliation":[{"name":"School of EE, KAIST"}]},{"given":"Changho","family":"Suh","sequence":"additional","affiliation":[{"name":"School of EE, KAIST"}]},{"given":"Kannan","family":"Ramchandran","sequence":"additional","affiliation":[{"name":"Dept. of EECS, UC Berkeley"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Subsampled Rényi differential privacy and analytical moments accountant","author":"wang","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.11"},{"key":"ref31","article-title":"SGD on random mixtures: Private machine learning under data-breach threats","author":"lee","year":"2018","journal-title":"ICLR Workshop"},{"key":"ref30","article-title":"Data augmentation by pairing samples for images classification","author":"inoue","year":"2018"},{"key":"ref37","first-page":"i?71","article-title":"Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes","author":"shamir","year":"2013","journal-title":"International Conference on International Conference on Machine Learning (ICML)"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.06.018"},{"key":"ref35","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref34","article-title":"The MNIST database of handwritten digits","author":"lecun","year":"0"},{"key":"ref10","article-title":"Privacy via the Johnson-Lindenstrauss Transform","author":"kenthapadi","year":"2012","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2737966"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.247"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3134428"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350253"},{"key":"ref15","first-page":"289","article-title":"Privacy-preserving logistic regression","author":"chaudhuri","year":"2009","journal-title":"Advances in neural information processing systems"},{"key":"ref16","first-page":"4130","article-title":"Collect at once, use effectively: Making non-interactive locally private learning possible","author":"zheng","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref17","first-page":"32","article-title":"The price of differential privacy for online learning","author":"agarwal","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref18","first-page":"478","article-title":"Differentially private learning of undirected graphical models using collective graphical models","author":"bernstein","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref19","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"chaudhuri","year":"2011","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"ref28","article-title":"Between-class learning for image classification","author":"tokozume","year":"2017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67786-6_6"},{"key":"ref27","article-title":"Learning from between-class examples for deep sound recognition","author":"tokozume","year":"2018","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2697856"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335438"},{"key":"ref29","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref8","first-page":"193","article-title":"A framework for high-accuracy privacy-preserving mining","author":"agrawal","year":"2005","journal-title":"Data Engineering 2005 ICDE 2005 Proceedings 21st International Conference on"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066187"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1749603.1749605"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1142351.1142373"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref22","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"chaudhuri","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref21","article-title":"Private empirical risk mini-mization: Efficient algorithms and tight error bounds","author":"bassily","year":"2014","journal-title":"Proceedings of the 55th Annual Symposium on the Foundations of Computer Science (FOCS’14)"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2017.10"},{"key":"ref26","first-page":"5434","article-title":"Straggler mitigation in distributed optimization through data encoding","author":"karakus","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref25","article-title":"Semi-supervised knowledge transfer for deep learning from private training data","author":"papernot","year":"2017","journal-title":"International Conference on Learning Representations (ICLR)"}],"event":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","location":"Paris, France","start":{"date-parts":[[2019,7,7]]},"end":{"date-parts":[[2019,7,12]]}},"container-title":["2019 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8827389\/8849208\/08849381.pdf?arnumber=8849381","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:35:44Z","timestamp":1773347744000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8849381\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/isit.2019.8849381","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]}}}