Statistical confidentiality: Optimization techniques to protect tables

Statistical agencies collect individually identifiable data, process them, and publish statistical summaries (tables). During this process, the agencies are required to protect individually identifiable data through a variety of policies. In all cases, the scope is to provide the data users with useful statistical information, and to assure that the responses from the individuals are protected. To this end, and due to the size of the data, combinatorial problems appear and require algorithmic approaches to find optimal or near-optimal solutions. This article summarizes the main optimization problems and the most relevant contributions from operations research in this area.