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$\epsilon$-Differential privacy (DP) is a well-known privacy model thatoffers strong privacy guarantees. However, when applied to data releases, DPsignificantly deteriorates the analytical utility of the protected outcomes. Tokeep data utility at reasonable levels, practical applications of DP to datareleases have used weak privacy parameters (large $\epsilon$), which dilute theprivacy guarantees of DP. In this work, we tackle this issue by using analternative formulation of the DP privacy guarantees, named$\epsilon$-individual differential privacy (iDP), which causes less datadistortion while providing the same protection as DP to subjects. We enforceiDP in data releases by relying on attribute masking plus a pre-processing stepbased on data microaggregation. The goal of this step is to reduce thesensitivity to record changes, which determines the amount of noise required toenforce iDP (and DP). Specifically, we propose data microaggregation strategiesdesigned for iDP whose sensitivities are significantly lower than those used inDP. As a result, we obtain iDP-protected data with significantly better utilitythan with DP. We report on experiments that show how our approach can providestrong privacy (small $\epsilon$) while yielding protected data that do notsignificantly degrade the accuracy of secondary data analysis.

Conciliating Privacy and Utility in Data Releases via Individual Differential Privacy and Microaggregation