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Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data

Author

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  • Xiao-Bai Li

    (College of Management, University of Massachusetts Lowell, Lowell, Massachusetts 01854)

  • Sumit Sarkar

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

Abstract

To respond to growing concerns about privacy of personal information, organizations that use their customers' records in data-mining activities are forced to take actions to protect the privacy of the individuals involved. A common practice for many organizations today is to remove identity-related attributes from the customer records before releasing them to data miners or analysts. We investigate the effect of this practice and demonstrate that many records in a data set could be uniquely identified even after identity-related attributes are removed. We propose a perturbation method for categorical data that can be used by organizations to prevent or limit disclosure of confidential data for identifiable records when the data are provided to analysts for classification, a common data-mining task. The proposed method attempts to preserve the statistical properties of the data based on privacy protection parameters specified by the organization. We show that the problem can be solved in two phases, with a linear programming formulation in Phase I (to preserve the first-order marginal distribution), followed by a simple Bayes-based swapping procedure in Phase II (to preserve the joint distribution). Experiments conducted on several real-world data sets demonstrate the effectiveness of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:17:y:2006:i:3:p:254-270
    DOI: 10.1287/isre.1060.0095
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    References listed on IDEAS

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    1. Sumit Dutta Chowdhury & George T. Duncan & Ramayya Krishnan & Stephen F. Roehrig & Sumitra Mukherjee, 1999. "Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators," Management Science, INFORMS, vol. 45(12), pages 1710-1723, December.
    2. Rathindra Sarathy & Krishnamurty Muralidhar, 2002. "The Security of Confidential Numerical Data in Databases," Information Systems Research, INFORMS, vol. 13(4), pages 389-403, December.
    3. Krishnamurty Muralidhar & Rahul Parsa & Rathindra Sarathy, 1999. "A General Additive Data Perturbation Method for Database Security," Management Science, INFORMS, vol. 45(10), pages 1399-1415, October.
    4. Robert Garfinkel & Ram Gopal & Paulo Goes, 2002. "Privacy Protection of Binary Confidential Data Against Deterministic, Stochastic, and Insider Threat," Management Science, INFORMS, vol. 48(6), pages 749-764, June.
    5. 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. 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.
    2. Weiyin Hong & Frank K. Y. Chan & James Y. L. Thong, 2021. "Drivers and Inhibitors of Internet Privacy Concern: A Multidimensional Development Theory Perspective," Journal of Business Ethics, Springer, vol. 168(3), pages 539-564, January.
    3. Xiao-Bai Li & Sumit Sarkar, 2013. "Class-Restricted Clustering and Microperturbation for Data Privacy," Management Science, INFORMS, vol. 59(4), pages 796-812, April.
    4. Zike Cao & Kai-Lung Hui & Hong Xu, 2018. "An Economic Analysis of Peer Disclosure in Online Social Communities," Information Systems Research, INFORMS, vol. 29(3), pages 546-566, September.
    5. Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    6. Robert Garfinkel & Ram Gopal & Steven Thompson, 2007. "Releasing Individually Identifiable Microdata with Privacy Protection Against Stochastic Threat: An Application to Health Information," Information Systems Research, INFORMS, vol. 18(1), pages 23-41, March.
    7. Shaobo Li & Matthew J. Schneider & Yan Yu & Sachin Gupta, 2023. "Reidentification Risk in Panel Data: Protecting for k -Anonymity," Information Systems Research, INFORMS, vol. 34(3), pages 1066-1088, September.
    8. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    9. Xiao-Bai Li & Sumit Sarkar, 2011. "Protecting Privacy Against Record Linkage Disclosure: A Bounded Swapping Approach for Numeric Data," Information Systems Research, INFORMS, vol. 22(4), pages 774-789, December.
    10. Haibing Lu & Jaideep Vaidya & Vijayalakshmi Atluri & Yingjiu Li, 2015. "Statistical Database Auditing Without Query Denial Threat," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 20-34, February.

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