A new privacy-preserving distributed k-clustering algorithm

Geetha Jagannathan, Krishnan Pillaipakkamnatt, Rebecca N. Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

86 Scopus citations

Abstract

We present a simple I/O-efficient k-clustering algorithm that was designed with the goal of enabling a privacy-preserving version of the algorithm. Our experiments show that this algorithm produces cluster centers that are, on average, more accurate than the ones produced by the well known iterative k-means algorithm. We use our new algorithm as the basis for a communication-efficient privacy-preserving k-clustering protocol for databases that are horizontally partitioned between two parties. Unlike existing privacy-preserving protocols based on the k-means algorithm, this protocol does not reveal intermediate candidate cluster centers.

Original languageEnglish (US)
Title of host publicationProceedings of the Sixth SIAM International Conference on Data Mining
PublisherSociety for Industrial and Applied Mathematics
Pages494-498
Number of pages5
ISBN (Print)089871611X, 9780898716115
DOIs
StatePublished - 2006
Externally publishedYes
EventSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
Duration: Apr 20 2006Apr 22 2006

Publication series

NameProceedings of the Sixth SIAM International Conference on Data Mining
Volume2006

Other

OtherSixth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityBethesda, MD
Period4/20/064/22/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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