Distributed model based sampling technique for privacy preserving clustering

Xiao Dan Wu, Dian Min Yue, Feng Li Liu, Chao Hsien Chu

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

2 Scopus citations

Abstract

The sharing of data has been proven beneficial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new method for privacy-preserving clustering based on the probability distributed model for clustering. This paper proposes square wave-based clustering model, gauss distribution-based clustering model and the multivariate normal distribution-based clustering model based on the information provided by the K-means clustering result separately. And we mainly show the correctness and feasibility of the sampled data for clustering by experiment. This new preserving technique can be used not only for distributed clustering, but also for simultaneous clustering rule and data hiding.

Original languageEnglish (US)
Title of host publicationProceedings of 2007 International Conference on Management Science and Engineering, ICMSE'07 (14th)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages192-197
Number of pages6
ISBN (Print)9787883580805
DOIs
StatePublished - Jan 1 2007
Event2007 International Conference on Management Science and Engineering, ICMSE'07 - Harbin, China
Duration: Aug 20 2007Aug 22 2007

Publication series

NameProceedings of 2007 International Conference on Management Science and Engineering, ICMSE'07 (14th)

Other

Other2007 International Conference on Management Science and Engineering, ICMSE'07
Country/TerritoryChina
CityHarbin
Period8/20/078/22/07

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • General Decision Sciences
  • Engineering (miscellaneous)

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