Inferential disclosure limitation in multivariate categorical databases

Protecting data against inferential disclosure is a significant research challenge. With the increasing pervasiveness of data-warehouses and On-Line-Analytical-Processing applications, disclosure limitation in multidimensional databases is especially important. Recent research has proposed efficient methods for inferential disclosure detection but has not addressed the problem of disclosure elimination. Disclosures are removed by additive noise data perturbation. The goal is to minimize information loss due to data perturbation. This article formulates the disclosure elimination problem in multidimensional categorical databases as a constrained optimization model. Since finding optimal solutions to the resulting problem is computationally hard, a genetic algorithm (GA) is used to identify good feasible solutions. Results indicate that the proposed GA based approach can efficiently identify good feasible solutions that require low levels of data perturbation.