How to group attributes in multivariate microaggregation

Jordi Nin, Javier Herranz, Vicenç Torra

Producció científica: Article en revista indexadaArticleAvaluat per experts

16 Cites (Scopus)


Microaggregation is one of the most employed microdata protection methods. It builds clusters of at least k original records, and then replaces these records with the centroid of the cluster. When the number of attributes of the dataset is large, one usually splits the dataset into smaller blocks of attributes, and then applies microaggregation to each block, successively and independently. In this way, the effect of the noise introduced by microaggregation is reduced, at the cost of losing the k-anonymity property. In this work we show that, besides the specific microaggregation method, the value of the parameter k and the number of blocks in which the dataset is split, there exists another factor which influences the quality of the microaggregation: the way in which the attributes are grouped to form the blocks. When correlated attributes are grouped in the same block, the statistical utility of the protected dataset is higher. In contrast, when correlated attributes are dispersed into different blocks, the achieved anonymity is higher, and so, the disclosure risk is lower. We present quantitative evaluations of such statements based on different experiments on real datasets.

Idioma originalAnglès
Pàgines (de-a)121-138
Nombre de pàgines18
RevistaInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systems
NúmeroSUPPL. 1
Estat de la publicacióPublicada - d’abr. 2008
Publicat externament


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