A new scheme for distributed density estimation based privacy-preserving clustering

Chunhua Su, Feng Bao, Jianying Zhou, Tsuyoshi Takagi, Kouichi Sakurai

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

6 Citations (Scopus)

Abstract

The sensitive information leakage and security risk is a problem from which both individual and enterprise suffer in massive data collection and the information retrieval by the distrusted parties. In this paper, we focus on the privacy issue of data clustering and point out some security risks in the existing data mining algorithms. Associated with cryptographic techniques, we initiate an application of random data perturbation (RDP) which has been widely used for preserving the privacy of individual records in statistical database for the distributed data clustering scheme. Our scheme applies linear transformation of Gaussian distribution perturbed data and general additional data perturbation (GADP) schemes to preserve the privacy for distributed kernel density estimation with the help of any trusted third party. We also show that our scheme is more secure against the random matrix-based filtering attack which is based on analysis of the distribution of the eigenvalues by using two RDP methods.

Original languageEnglish
Title of host publicationARES 2008 - 3rd International Conference on Availability, Security, and Reliability, Proceedings
Pages112-119
Number of pages8
DOIs
Publication statusPublished - Aug 14 2008
Event3rd International Conference on Availability, Security, and Reliability, ARES 2008 - Barcelona, Spain
Duration: Mar 4 2008Mar 7 2008

Publication series

NameARES 2008 - 3rd International Conference on Availability, Security, and Reliability, Proceedings

Other

Other3rd International Conference on Availability, Security, and Reliability, ARES 2008
CountrySpain
CityBarcelona
Period3/4/083/7/08

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Safety, Risk, Reliability and Quality

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