Conciliating Privacy and Utility in Data Releases via Individual Differential Privacy and Microaggregation. (arXiv:2312.13712v1 [cs.CR])

$\epsilon$-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 …

applications data differential privacy outcomes privacy releases utility well-known