Using randomized response techniques for privacy-preserving data mining

Wenliang Du, Zhijun Zhan

Research output: Contribution to conferencePaper

202 Scopus citations

Abstract

Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy. We also show how the parameter used in the randomized response techniques affects the accuracy of the results.

Original languageEnglish (US)
Pages505-510
Number of pages6
DOIs
StatePublished - Dec 1 2003
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: Aug 24 2003Aug 27 2003

Other

Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
CountryUnited States
CityWashington, DC
Period8/24/038/27/03

Keywords

  • Data mining
  • Decision tree
  • Privacy
  • Security

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Du, W., & Zhan, Z. (2003). Using randomized response techniques for privacy-preserving data mining. 505-510. Paper presented at 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, Washington, DC, United States. https://doi.org/10.1145/956750.956810