  Microaggregation is a technique for disclosure limitation aimed at protecting
the privacy of data subjects in microdata releases. It has been used as an
alternative to generalization and suppression to generate $k$-anonymous data
sets, where the identity of each subject is hidden within a group of $k$
subjects. Unlike generalization, microaggregation perturbs the data and this
additional masking freedom allows improving data utility in several ways, such
as increasing data granularity, reducing the impact of outliers and avoiding
discretization of numerical data. $k$-Anonymity, on the other side, does not
protect against attribute disclosure, which occurs if the variability of the
confidential values in a group of $k$ subjects is too small. To address this
issue, several refinements of $k$-anonymity have been proposed, among which
$t$-closeness stands out as providing one of the strictest privacy guarantees.
Existing algorithms to generate $t$-close data sets are based on generalization
and suppression (they are extensions of $k$-anonymization algorithms based on
the same principles). This paper proposes and shows how to use microaggregation
to generate $k$-anonymous $t$-close data sets. The advantages of
microaggregation are analyzed, and then several microaggregation algorithms for
$k$-anonymous $t$-closeness are presented and empirically evaluated.
