  Being able to release and exploit open data gathered in information systems
is crucial for researchers, enterprises and the overall society. Yet, these
data must be anonymized before release to protect the privacy of the subjects
to whom the records relate. Differential privacy is a privacy model for
anonymization that offers more robust privacy guarantees than previous models,
such as $k$-anonymity and its extensions. However, it is often disregarded that
the utility of differentially private outputs is quite limited, either because
of the amount of noise that needs to be added to obtain them or because utility
is only preserved for a restricted type and/or a limited number of queries. On
the contrary, $k$-anonymity-like data releases make no assumptions on the uses
of the protected data and, thus, do not restrict the number and type of doable
analyses. Recently, some authors have proposed mechanisms to offer
general-purpose differentially private data releases. This paper extends such
works with a specific focus on the preservation of the utility of the protected
data. Our proposal builds on microaggregation-based anonymization, which is
more flexible and utility-preserving than alternative anonymization methods
used in the literature, in order to reduce the amount of noise needed to
satisfy differential privacy. In this way, we improve the utility of
differentially private data releases. Moreover, the noise reduction we achieve
does not depend on the size of the data set, but just on the number of
attributes to be protected, which is a more desirable behavior for large data
sets. The utility benefits brought by our proposal are empirically evaluated
and compared with related works for several data sets and metrics.
