  $\epsilon$-Differential privacy (DP) is a well-known privacy model that
offers strong privacy guarantees. However, when applied to data releases, DP
significantly deteriorates the analytical utility of the protected outcomes. To
keep data utility at reasonable levels, practical applications of DP to data
releases have used weak privacy parameters (large $\epsilon$), which dilute the
privacy guarantees of DP. In this work, we tackle this issue by using an
alternative formulation of the DP privacy guarantees, named
$\epsilon$-individual differential privacy (iDP), which causes less data
distortion while providing the same protection as DP to subjects. We enforce
iDP in data releases by relying on attribute masking plus a pre-processing step
based on data microaggregation. The goal of this step is to reduce the
sensitivity to record changes, which determines the amount of noise required to
enforce iDP (and DP). Specifically, we propose data microaggregation strategies
designed for iDP whose sensitivities are significantly lower than those used in
DP. As a result, we obtain iDP-protected data with significantly better utility
than with DP. We report on experiments that show how our approach can provide
strong privacy (small $\epsilon$) while yielding protected data that do not
significantly degrade the accuracy of secondary data analysis.
