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Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators

Listed author(s):
  • Sumit Dutta Chowdhury

    (605 West View Terrace, Alexandria, Virginia 22301)

  • George T. Duncan

    (The H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Ramayya Krishnan

    (The H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Stephen F. Roehrig

    (The H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Sumitra Mukherjee

    (School of Computer and Information Systems, Nova University, Fort Lauderdale, Florida 33315)

Registered author(s):

    As databases grow more prevalent and comprehensive, database administrators seek to limit disclosure of confidential information while still providing access to data. Practical databases accommodate users with heterogeneous needs for access. Each class of data user is accorded access to only certain views. Other views are considered confidential, and hence to be protected. Using illustrations from health care and education, this article addresses inferential disclosure of confidential views in multidimensional categorical databases. It demonstrates that any structural, so data-value-independent method for detecting disclosure can fail. Consistent with previous work for two-way tables, it presents a data-value-dependent method to obtain tight lower and upper bounds for confidential data values. For two-dimensional projections of categorical databases, it exploits the network structure of a linear programming (LP) formulation to develop two transportation flow algorithms that are both computationally efficient and insightful. These algorithms can be easily implemented through two new matrix operators, cell-maxima and cell-minima. Collectively, this method is called matrix comparative assignment (MCA). Finally, it extends both the LP and MCA approaches to inferential disclosure when accessible views have been masked.

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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 45 (1999)
    Issue (Month): 12 (December)
    Pages: 1710-1723

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    Handle: RePEc:inm:ormnsc:v:45:y:1999:i:12:p:1710-1723
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    1. 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.
    2. 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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