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Exact and heuristic methods for cell suppression in multi-dimensional linked tables

Author

Listed:
  • S F Roehrig

    (Carnegie Mellon University)

  • R Padman

    (Carnegie Mellon University)

  • R Krishnan

    (Carnegie Mellon University)

  • G T Duncan

    (Carnegie Mellon University)

Abstract

The increasing demand for information, coupled with the increasing capability of computer systems, has compelled information providers to reassess their procedures for preventing disclosure of confidential information. This paper considers the problem of protecting an unpublished, sensitive table by suppressing cells in related, published tables. A conventional integer programming technique for two-dimensional tables is extended to find an optimal suppression set for the public tables. This can be used to protect the confidentiality of sensitive data in three- and higher-dimensional tables. More importantly, heuristics that are intimately related to the structure of the problem are also presented to mitigate the computational difficulty of the integer program. An example is drawn from healthcare management. Data tables are randomly generated to assess the computational time/space restrictions of the IP model, and to evaluate the heuristics.

Suggested Citation

  • S F Roehrig & R Padman & R Krishnan & G T Duncan, 2011. "Exact and heuristic methods for cell suppression in multi-dimensional linked tables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 291-304, February.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:2:d:10.1057_jors.2010.133
    DOI: 10.1057/jors.2010.133
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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. 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.
    3. 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.
    4. James Kelly & Bruce Golden & Arjang Assad, 1990. "Using Simulated Annealing to Solve Controlled Rounding Problems," INFORMS Journal on Computing, INFORMS, vol. 2(2), pages 174-185, May.
    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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