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)


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.


Rule induction Imprecise rules MLEM2 Privacy protection k-anonymity 



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


  1. 1.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)Google Scholar
  2. 2.
    Pawlak, Z.: Rough Sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  3. 3.
    Inuiguchi, M., Hamakawa, T.: The utilities of imprecise rules and redundant rules for classifiers. In: Huynh, V.-N., et al. (eds.) Knowledge and Systems Engineering. AISC, vol. 245, pp. 45–56. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Hamakawa, T, Inuiguchi, M.: On the Utility of Imprecise Rules Induced by MLEM2 in Classification. In: Proceedings of 2014 IEEE International Conference on Granular Computing C, pp. 76–81. IEEE Xplore (2014)Google Scholar
  5. 5.
    Domingo-Ferrer, J., Torra, V.: Disclosure control methods and information loss for microdata, confidentiality, disclosure, and data access. In: Doyle, P., et al. (eds.) Theory and Practical Applications for Statistical Agencies, pp. 91–110. Elsevier, Amsterdam (2001)Google Scholar
  6. 6.
    Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  7. 7.
    Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertainty, Fuzziness Knowl. Based Sys. 10(5), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  9. 9.
    Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Yakoubov, S., Gadepally, V., Schear, N., Shen, E., Yerukhimovich, A.: A survey of cryptographic approaches to securing big-data analytics in the cloud. In: Proceedings of 2014 IEEE High Performance Extreme Computing Conference, pp. 1–6. IEEE Xplore (2014)Google Scholar
  11. 11.
    Zhou, Z., Huang, L., Yun, Y.: Privacy preserving attribute reduction based on rough set. In: Proceedings of 2nd International Workshop on Knowledge Discovery and Data Mining. WKKD 2009, pp. 202–206. AAAI, Portland (2009)Google Scholar
  12. 12.
    Rokach, L., Schclar, A.: k-anonymized reducts. In: Proceedings of 2010 IEEE International Conference on Granular Computing, pp. 392–395. IEEE Xplore (2010)Google Scholar
  13. 13.
    Ye, M., Wu, X., Hu, X., Hu, D.: Anonymizing classification data using rough set theory. Knowl. Based Sys. 43, 82–94 (2013)CrossRefGoogle Scholar
  14. 14.
    Grzymala-Busse, J.W.: MLEM2 - discretization during rule induction. In: Klopotek, M.A., Wierzchon, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. AISC, vol. 22, pp. 499–508. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    UCI Machine Learning Repository.

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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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