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Privacy Protection of Binary Confidential Data Against Deterministic, Stochastic, and Insider Threat

Author

Listed:
  • Robert Garfinkel

    (School of Business Administration, University of Connecticut, Storrs, Connecticut 06269)

  • Ram Gopal

    (School of Business Administration, University of Connecticut, Storrs, Connecticut 06269)

  • Paulo Goes

    (School of Business Administration, University of Connecticut, Storrs, Connecticut 06269)

Abstract

A practical model and an associated method are developed for providing consistent, deterministically correct responses to ad-hoc queries to a database containing a field of binary confidential data. COUNT queries, i.e., the number of selected subjects whose confidential datum is positive, are to be answered. Exact answers may allow users to determine an individual's confidential information. Instead, the proposed technique gives responses in the form of a number plus a guarantee so that the user can determine an interval that is sure to contain the exact answer. At the same time, the method is also able to provide both deterministic and stochastic protection of the confidential data to the subjects of the database. Insider threat is defined precisely and a simple option for defense against it is given. Computational results on a simulated database are very encouraging in that most queries are answered with tight intervals, and that the quality of the responses improves with the number of subjects identified by the query. Thus the results are very appropriate for the very large databases prevalent in business and governmental organizations. The technique is very efficient in terms of both time and storage requirements, and is readily scalable and implementable.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormnsc:v:48:y:2002:i:6:p:749-764
    DOI: 10.1287/mnsc.48.6.749.193
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    References listed on IDEAS

    as
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Syam Menon & Abhijeet Ghoshal & Sumit Sarkar, 2022. "Modifying Transactional Databases to Hide Sensitive Association Rules," Information Systems Research, INFORMS, vol. 33(1), pages 152-178, March.
    2. Xue Bai & Ram Gopal & Manuel Nunez & Dmitry Zhdanov, 2012. "On the Prevention of Fraud and Privacy Exposure in Process Information Flow," INFORMS Journal on Computing, INFORMS, vol. 24(3), pages 416-432, August.
    3. 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.
    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.
    6. Syam Menon & Sumit Sarkar, 2007. "Minimizing Information Loss and Preserving Privacy," Management Science, INFORMS, vol. 53(1), pages 101-116, January.
    7. Xiao-Bai Li & Sumit Sarkar, 2013. "Class-Restricted Clustering and Microperturbation for Data Privacy," Management Science, INFORMS, vol. 59(4), pages 796-812, April.
    8. Amalia R. Miller & Catherine Tucker, 2009. "Privacy Protection and Technology Diffusion: The Case of Electronic Medical Records," Management Science, INFORMS, vol. 55(7), pages 1077-1093, July.
    9. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    10. Joseph B. Kadane & Ramayya Krishnan & Galit Shmueli, 2006. "A Data Disclosure Policy for Count Data Based on the COM-Poisson Distribution," Management Science, INFORMS, vol. 52(10), pages 1610-1617, October.
    11. 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.
    12. 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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