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Against Classification Attacks: A Decision Tree Pruning Approach to Privacy Protection in Data Mining

Author

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  • Xiao-Bai Li

    (Department of Operations and Information Systems, University of Massachusetts Lowell, Lowell, Massachusetts 01854)

  • Sumit Sarkar

    (School of Management, The University of Texas at Dallas, Richardson, Texas 75080)

Abstract

Data-mining techniques can be used not only to study collective behavior about customers, but also to discover private information about individuals. In this study, we demonstrate that decision trees, a popular classification technique for data mining, can be used to effectively reveal individuals' confidential data, even when the identities of the individuals are not present in the data. We propose a novel approach for organizations to protect confidential data from such a classification attack. The key components of this approach include a set of entropy-based measures to evaluate disclosure risks of individual records, an optimal pruning algorithm to identify high-risk records, and a pair of data-swapping procedures to reduce the disclosure risks. The proposed method provides the best trade-off between data utility and privacy protection against classification attacks. It can be applied to data with both numeric and categorical attributes. An experimental study on six real-world data sets shows that the proposed method is very effective in protecting privacy while enabling legitimate data mining and analysis.

Suggested Citation

  • Xiao-Bai Li & Sumit Sarkar, 2009. "Against Classification Attacks: A Decision Tree Pruning Approach to Privacy Protection in Data Mining," Operations Research, INFORMS, vol. 57(6), pages 1496-1509, December.
  • Handle: RePEc:inm:oropre:v:57:y:2009:i:6:p:1496-1509
    DOI: 10.1287/opre.1090.0702
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    References listed on IDEAS

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    1. Manuel A. Nunez & Robert S. Garfinkel & Ram D. Gopal, 2007. "Stochastic Protection of Confidential Information in Databases: A Hybrid of Data Perturbation and Query Restriction," Operations Research, INFORMS, vol. 55(5), pages 890-908, October.
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    3. Xiao-Bai Li & Sumit Sarkar, 2006. "Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data," Information Systems Research, INFORMS, vol. 17(3), pages 254-270, September.
    4. Duncan, George & Lambert, Diane, 1989. "The Risk of Disclosure for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 207-217, April.
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    Cited by:

    1. Mingzheng Wang & Zhengrui Jiang & Haifang Yang & Yu Zhang, 2018. "T -Closeness Slicing: A New Privacy-Preserving Approach for Transactional Data Publishing," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 438-453, August.

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