  Environmental Insights Explorer (EIE) is a Google product that reports
aggregate statistics about human mobility, including various methods of transit
used by people across roughly 50,000 regions globally. These statistics are
used to estimate carbon emissions and provided to policymakers to inform their
decisions on transportation policy and infrastructure. Due to the inherent
sensitivity of this type of user data, it is crucial that the statistics
derived and released from it are computed with appropriate privacy protections.
In this work, we use a combination of federated analytics and differential
privacy to release these required statistics, while operating under strict
error constraints to ensure utility for downstream stakeholders. In this work,
we propose a new mechanism that achieves $ \epsilon \approx 2 $-DP while
satisfying these strict utility constraints, greatly improving over natural
baselines. We believe this mechanism may be of more general interest for the
broad class of group-by-sum workloads.
