An adaptive algorithm for multivariate data-oriented microaggregation

Microaggregation for Statistical Disclosure Control (SDC) has been shown to be an efficient method to hamper individual identification. Indeed, micro data are wrapped in such a way that can be published and mined without providing any private information that can be linked to specific individuals. In this respect, a microaggregation method would seek to lower the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. In this paper, we introduce a new algorithm, called AdMicro-FSOM for the multivariate microaggregation task. The main thrust of this algorithm stands in its handling fuzzy partition into a microaggregation method. The extensive carried out experiments show the obtention of low information loss, even when handling noisy data. In addition, the obtained results sharply outperform those obtained by the pioneering algorithms of the dedicated literature.