{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T04:59:12Z","timestamp":1773377952321,"version":"3.50.1"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"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":[[2021,7,12]]},"DOI":"10.1109\/isit45174.2021.9518014","type":"proceedings-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T16:52:42Z","timestamp":1630515162000},"page":"332-337","source":"Crossref","is-referenced-by-count":0,"title":["Differentially-Private Sublinear-Time Clustering"],"prefix":"10.1109","author":[{"given":"Jeremiah","family":"Blocki","sequence":"first","affiliation":[{"name":"Purdue University,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Grigorescu","sequence":"additional","affiliation":[{"name":"Purdue University,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamalika","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Purdue University,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2857705.2857708"},{"key":"ref10","volume":"30","author":"czumaj","year":"2007","journal-title":"Sublinear-time approximation algorithms for clustering via random sampling"},{"key":"ref11","year":"2017","journal-title":"Learning With Privacy at Scale"},{"key":"ref12","volume":"7","author":"dwork","year":"2016","journal-title":"Calibrating Noise to Sensitivity in Private Data Analysis"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"ref14","article-title":"Private coresets","author":"dan","year":"0","journal-title":"STOC"},{"key":"ref15","article-title":"Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks","author":"dan","year":"0","journal-title":"IPSN"},{"key":"ref16","article-title":"Differentially private clustering: Tight approximation ratios","author":"badih","year":"0","journal-title":"NeurIPS"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973075.90"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1007352.1007400"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3196959.3196977"},{"key":"ref28","article-title":"Private k-means clustering with stability assumptions","author":"shechner","year":"0","journal-title":"AISTATS"},{"key":"ref4","article-title":"Differentially private clustering in high-dimensional Euclidean spaces","author":"balcan","year":"0","journal-title":"ICML"},{"key":"ref27","article-title":"The effectiveness of lloyd-type methods for the k-means problem","volume":"59","author":"rafail","year":"2012","journal-title":"Journal of the ACM (JACM)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.36"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132769"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975994.33"},{"key":"ref5","article-title":"Privacy amplification by subsampling: Tight analyses via couplings and divergences","author":"balle","year":"0","journal-title":"NeurIPS"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1109557.1109687"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.2002.1882"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/380752.380755"},{"key":"ref9","article-title":"A constant factor approximation algorithm for k-median clustering with outliers","author":"chen","year":"0","journal-title":"SODA"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1137\/18M1171321"},{"key":"ref20","article-title":"Differentially private k-means with constant multiplicative error","author":"kaplan","year":"0","journal-title":"NeurIPS"},{"key":"ref22","article-title":"Guidelines for implementing and auditing differentially private systems","volume":"abs 2002 4049","author":"kifer","year":"2020","journal-title":"CoRR"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/090756090"},{"key":"ref24","article-title":"Sublinear time approximate clustering","author":"mishra","year":"0","journal-title":"SODA"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1137\/130938645"},{"key":"ref26","article-title":"Clustering algorithms for the centralized and local models","author":"nissim","year":"0","journal-title":"ALT"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1250790.1250803"}],"event":{"name":"2021 IEEE International Symposium on Information Theory (ISIT)","location":"Melbourne, Australia","start":{"date-parts":[[2021,7,12]]},"end":{"date-parts":[[2021,7,20]]}},"container-title":["2021 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9517708\/9517709\/09518014.pdf?arnumber=9518014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:35:40Z","timestamp":1773347740000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9518014\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,12]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/isit45174.2021.9518014","relation":{},"subject":[],"published":{"date-parts":[[2021,7,12]]}}}