Constrained Microaggregation: Adding Constraints for Data Editing

Privacy preserving data mining and statistical disclosure control have introduced several methods for data perturbation that can be used for ensuring the privacy of data respondents. Such methods, as rank swapping and microaggregation, perturbate the data introducing some kind of noise. Nevertheless, it is usual that data are edited with care after collection to remove inconsistencies, and such perturbation might cause the introduction of new inconsistencies to them. In this paper we study the development of methods for microaggregation that avoid the introduction of such inconsistencies. That is, methods that ensure the protected data to satisfy a set of given constraints.