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 $k$<alternatives><inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq1-2435777.gif"/> </alternatives>-anonymous data sets, where the identity of each subject is hidden within a group of $k$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq2-2435777.gif"/></alternatives> 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. $k$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq3-2435777.gif"/></alternatives>-Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of $k$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq4-2435777.gif"/></alternatives> subjects is too small. To address this issue, several refinements of $k$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq5-2435777.gif"/></alternatives>-anonymity have been proposed, among which $t$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq6-2435777.gif"/></alternatives>-closeness stands out as providing one of the strictest privacy guarantees. Existing algorithms to generate $t$<alternatives><inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq7-2435777.gif"/></alternatives> -close data sets are based on generalization and suppression (they are extensions of $k$<alternatives><inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq8-2435777.gif"/> </alternatives>-anonymization algorithms based on the same principles). This paper proposes and shows how to use microaggregation to generate $k$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq9-2435777.gif"/></alternatives>-anonymous $t$<alternatives><inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq10-2435777.gif"/> </alternatives>-close data sets. The advantages of microaggregation are analyzed, and then several microaggregation algorithms for $k$<alternatives> <inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq11-2435777.gif"/></alternatives>-anonymous $t$<alternatives><inline-graphic xlink:type="simple" xlink:href="soriacomas-ieq12-2435777.gif"/> </alternatives>-closeness are presented and empirically evaluated.