Cell suppression to limit content-based disclosure

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. General logical and numerical methods exist to determine, prior to release, if disclosure can occur-either directly or through inference. One method uses linear programming techniques applied to multi-dimensional tables of count data to determine which cells are subject to inferential disclosure. This paper develops integer programming (IP) techniques to find an optimal primary suppression set for protecting the confidentiality of sensitive data in 3D tables. An example is drawn from Federal Reserve Bank records. Data tables are randomly generated to assess the extent of inferential disclosure and the computational time/space restrictions of the IP model