  We present a data analytics system that ensures accurate counts can be
released with differential privacy and minimal onboarding effort while showing
instances that outperform other approaches that require more onboarding effort.
The primary difference between our proposal and existing approaches is that it
does not rely on user contribution bounds over distinct elements, i.e.
$\ell_0$-sensitivity bounds, which can significantly bias counts. Contribution
bounds for $\ell_0$-sensitivity have been considered as necessary to ensure
differential privacy, but we show that this is actually not necessary and can
lead to releasing more results that are more accurate. We require minimal
hyperparameter tuning and demonstrate results on several publicly available
dataset. We hope that this approach will help differential privacy scale to
many different data analytics applications.
