  Differentially private algorithms for common metric aggregation tasks, such
as clustering or averaging, often have limited practicality due to their
complexity or to the large number of data points that is required for accurate
results. We propose a simple and practical tool, $\mathsf{FriendlyCore}$, that
takes a set of points ${\cal D}$ from an unrestricted (pseudo) metric space as
input. When ${\cal D}$ has effective diameter $r$, $\mathsf{FriendlyCore}$
returns a "stable" subset ${\cal C} \subseteq {\cal D}$ that includes all
points, except possibly few outliers, and is {\em certified} to have diameter
$r$. $\mathsf{FriendlyCore}$ can be used to preprocess the input before
privately aggregating it, potentially simplifying the aggregation or boosting
its accuracy. Surprisingly, $\mathsf{FriendlyCore}$ is light-weight with no
dependence on the dimension. We empirically demonstrate its advantages in
boosting the accuracy of mean estimation and clustering tasks such as $k$-means
and $k$-GMM, outperforming tailored methods.
