Disclosure detection in multivariate categorical databases: Auditing confidentiality protection through two new matrix operators

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 n eeds 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 categoric al databases. It demonstrates that any structural, so data-value-independen t method for detecting disclosure can fail. Consistent with previous work f or two-way tables, it presents a data-value-dependent method to obtain tigh t 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 al gorithms that are both computationally efficient and insightful. These algo rithms can be easily implemented through two new matrix operators, cell-max ima and cell-minima. Collectively, this method is called matrix comparative assignment (MCA). Finally, it extends both the LP and MCA approaches to in ferential disclosure when accessible views have been masked.