Identifying features that leak information about sensitive attributes is a
key challenge in the design of information obfuscation mechanisms. In this
paper, we propose a framework to identify information-leaking features via
information density estimation. Here, features whose information densities
exceed a pre-defined threshold are deemed information-leaking features. Once
these features are identified, we sequentially pass them through a targeted
obfuscation mechanism with a provable leakage guarantee in terms of
$\mathsf{E}_\gamma$-divergence. The core of this mechanism relies on a
data-driven estimate of the trimmed information density for which we propose a
novel estimator, named the trimmed information density estimator (TIDE). We
then use TIDE to implement our mechanism on three real-world datasets. Our
approach can be used as a data-driven pipeline for designing obfuscation
mechanisms targeting specific features.