Combined data distortion strategies for privacy-preserving data mining

Bo Peng, Xingyu Geng, Jun Zhang

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

9 Scopus citations

Abstract

The problem of privacy-preserving data mining has become more and more important in recent years. Many successful and efficient techniques have been developed. However, in collaborative data analysis, part of the datasets may come from different data owners and may be processed using different data distortion methods. Thus, combinations of datasets processed using different methods are of practical interests. In this paper, a class of novel data distortion strategies is proposed. Four schemes via attribute partition, with different combinations of singular value decomposition (SVD), nonnegative matrix factorization (NMF), discrete wavelet transformation (DWT), are designed to perturb submatrix of the original datasets for privacy protection. We use some metrics to measure the performance of the proposed new strategies. Data utility is examined by using a binary classification based on the support vector machine. Our experimental results indicate that, in comparison with the individual data distortion techniques, the proposed schemes are very efficient in achieving a good trade-off between data privacy and data utility, and provide a feasible solution for collaborative data analysis.

Original languageEnglish
Title of host publicationICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings
PagesV1572-V1576
DOIs
StatePublished - 2010
Event2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010 - Chengdu, China
Duration: Aug 20 2010Aug 22 2010

Publication series

NameICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings
Volume1

Conference

Conference2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010
Country/TerritoryChina
CityChengdu
Period8/20/108/22/10

Keywords

  • Data distortation
  • Data mining
  • Privacy preservation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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