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Confidentiality via Camouflage: The CVC Approach to Disclosure Limitation When Answering Queries to Databases

Author

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  • Ram Gopal

    (School of Business Administration, University of Connecticut, Department of Operations and Information Management, Storrs, Connecticut 06269-2041)

  • Robert Garfinkel

    (School of Business Administration, University of Connecticut, Department of Operations and Information Management, Storrs, Connecticut 06269-2041)

  • Paulo Goes

    (School of Business Administration, University of Connecticut, Department of Operations and Information Management, Storrs, Connecticut 06269-2041)

Abstract

A practical method is presented for giving unlimited, deterministically correct, numerical responses to ad-hoc queries to an online database, while not compromising confidential numerical data. The method is appropriate for any size database, and no assumptions are needed about the statistical distribution of the confidential data. Responses are in the form of a number plus a guarantee, so the user can determine an interval that is sure to contain the exact answer. Virtually any imaginable query type can be answered, and in the absence of insider information, collusion among the users presents no problem. Experimental analysis supports the practical viability of the proposed method.

Suggested Citation

  • Ram Gopal & Robert Garfinkel & Paulo Goes, 2002. "Confidentiality via Camouflage: The CVC Approach to Disclosure Limitation When Answering Queries to Databases," Operations Research, INFORMS, vol. 50(3), pages 501-516, June.
    • Handle: RePEc:inm:oropre:v:50:y:2002:i:3:p:501-516
    DOI: 10.1287/opre.50.3.501.7745
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    References listed on IDEAS

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    1. Ram D. Gopal & Paulo B. Goes & Robert S. Garfinkel, 1998. "Interval Protection of Confidential Information in a Database," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 309-322, August.
    2. Krishnamurty Muralidhar & Dinesh Batra & Peeter J. Kirs, 1995. "Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach," Management Science, INFORMS, vol. 41(9), pages 1549-1564, September.
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    Citations

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    Cited by:

    1. Xiao-Bai Li & Sumit Sarkar, 2013. "Class-Restricted Clustering and Microperturbation for Data Privacy," Management Science, INFORMS, vol. 59(4), pages 796-812, April.
    2. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    3. Han Li & Krishnamurty Muralidhar & Rathindra Sarathy, 2007. "Technical Note---Assessment of Disclosure Risk When Using Confidentiality via Camouflage," Operations Research, INFORMS, vol. 55(6), pages 1178-1182, December.
    4. Syam Menon & Sumit Sarkar & Shibnath Mukherjee, 2005. "Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns," Information Systems Research, INFORMS, vol. 16(3), pages 256-270, September.
    5. 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.

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