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Imprecise Rules for Data Privacy

  • Masahiro InuiguchiEmail author
  • Takuya Hamakawa
  • Seiki Ubukata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

When rules are induced, some rules can be supported only by a very small number of objects. Such rules often correspond to special cases so that supporting objects may be easily estimated. If the rules with small support include some sensitive data, this estimation of objects is not very good in the sense of data privacy. Considering this fact, we investigate utilization of imprecise rules for privacy protection in rule induction. Imprecise rules are rules classifying objects only into a set of possible classes. Utilizing imprecise rules, we propose an algorithm to induce k-anonymous rules, rules with k or more supporting objects. We demonstrate that the accuracy of the classifier with rules induced by the proposed algorithm is not worse than that of the classifier with rules induced by the conventional method. Moreover, the advantage of the proposed method with imprecise rules is examined by comparing other conceivable method with precise rules.

Keywords

Rule induction Imprecise rules MLEM2 Privacy protection k-anonymity 

Notes

Acknowledgment

This work was partially supported by JSPS KAKENHI Grant Number 26350423.

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Masahiro Inuiguchi
    • 1
    Email author
  • Takuya Hamakawa
    • 1
  • Seiki Ubukata
    • 2
  1. 1.Graduate School of Engineering ScienceOsaka University ToyonakaOsakaJapan
  2. 2.Graduate School of EngineeringOsaka Prefecture UniversityOsakaJapan

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