  We study the task of performing hierarchical queries based on summary reports
from the {\em Attribution Reporting API} for ad conversion measurement. We
demonstrate that methods from optimization and differential privacy can help
cope with the noise introduced by privacy guardrails in the API. In particular,
we present algorithms for (i) denoising the API outputs and ensuring
consistency across different levels of the tree, and (ii) optimizing the
privacy budget across different levels of the tree. We provide an experimental
evaluation of the proposed algorithms on public datasets.
