Importance of data standardization in privacy-preserving k-means clustering

Chunhua Su, Justin Zhan, Kouichi Sakurai

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

4 Citations (Scopus)

Abstract

Privacy-preserving k-means clustering assumes that there are at least two parties in the secure interactive computation. However, the existing schemes do not consider the data standardization which is an important task before executing the clustering among the different database. In this paper, we point out without data standardization, some problems will arise from many applications of data mining. Also, we provide a solution for the secure data standardization in the privacy-preserving k-means clustering.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2009 International Workshops
Subtitle of host publicationBenchmarX, MCIS, WDPP, PPDA, MBC, PhD
Pages276-286
Number of pages11
DOIs
Publication statusPublished - Sep 28 2009
EventInternational Workshops on Database Systems for Advanced Applications, DASFAA 2009: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD - Brisbane, QLD, Australia
Duration: Apr 20 2009Apr 23 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5667 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Database Systems for Advanced Applications, DASFAA 2009: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD
CountryAustralia
CityBrisbane, QLD
Period4/20/094/23/09

Fingerprint

Privacy Preserving
K-means Clustering
Standardization
Data mining
Data Mining
Clustering

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Su, C., Zhan, J., & Sakurai, K. (2009). Importance of data standardization in privacy-preserving k-means clustering. In Database Systems for Advanced Applications - DASFAA 2009 International Workshops: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD (pp. 276-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5667 LNCS). https://doi.org/10.1007/978-3-642-04205-8_23

Importance of data standardization in privacy-preserving k-means clustering. / Su, Chunhua; Zhan, Justin; Sakurai, Kouichi.

Database Systems for Advanced Applications - DASFAA 2009 International Workshops: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD. 2009. p. 276-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5667 LNCS).

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

Su, C, Zhan, J & Sakurai, K 2009, Importance of data standardization in privacy-preserving k-means clustering. in Database Systems for Advanced Applications - DASFAA 2009 International Workshops: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5667 LNCS, pp. 276-286, International Workshops on Database Systems for Advanced Applications, DASFAA 2009: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD, Brisbane, QLD, Australia, 4/20/09. https://doi.org/10.1007/978-3-642-04205-8_23
Su C, Zhan J, Sakurai K. Importance of data standardization in privacy-preserving k-means clustering. In Database Systems for Advanced Applications - DASFAA 2009 International Workshops: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD. 2009. p. 276-286. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-04205-8_23
Su, Chunhua ; Zhan, Justin ; Sakurai, Kouichi. / Importance of data standardization in privacy-preserving k-means clustering. Database Systems for Advanced Applications - DASFAA 2009 International Workshops: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD. 2009. pp. 276-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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