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

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Author Info

  • 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)

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    Abstract

    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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    File URL: http://dx.doi.org/10.1287/mnsc.45.12.1710
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    Bibliographic Info

    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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    Related research

    Keywords: confidentiality; data access; linear programming; matrix methods; disclosure risk; network models; disclosure limitation;

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    Cited by:
    1. Buzzigoli, Lucia & Giusti, Antonio, 2006. "From Marginals to Array Structure with the Shuttle Algorithm," MPRA Paper 49245, University Library of Munich, Germany.

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