Locating a small cluster privately

Kobbi Nissim, Uri Stemmer, Salil Vadhan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a new algorithm for locating a small cluster of points with differential privacy [Dwork, McSherry, Nissim, and Smith, 2006]. Our algorithm has implications to private data exploration, clustering, and removal of outliers. Furthermore, we use it to significantly relax the requirements of the sample and aggregate technique [Nissim, Raskhodnikova, and Smith, 2007], which allows compiling of "off the shelf" (non-private) analyses into analyses that preserve differential privacy.

Original languageEnglish
Title of host publicationPODS 2016 - Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
PublisherAssociation for Computing Machinery
Pages413-427
Number of pages15
ISBN (Electronic)9781450341912
DOIs
StatePublished - 15 Jun 2016
Externally publishedYes
Event35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2016 - San Francisco, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameProceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems
Volume26-June-01-July-2016

Conference

Conference35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2016
Country/TerritoryUnited States
CitySan Francisco
Period26/06/161/07/16

Keywords

  • Clustering
  • Differential privacy
  • Sample and aggregate

Cite this