  The Privacy Sandbox Attribution Reporting API has been recently deployed by
Google Chrome to support the basic advertising functionality of attribution
reporting (aka conversion measurement) after deprecation of third-party
cookies. The API implements a collection of privacy-enhancing guardrails
including contribution bounding and noise injection. It also offers flexibility
for the analyst to allocate the contribution budget.
  In this work, we present methods for optimizing the allocation of the
contribution budget for summary reports from the Attribution Reporting API. We
evaluate them on real-world datasets as well as on a synthetic data model that
we find to accurately capture real-world conversion data. Our results
demonstrate that optimizing the parameters that can be set by the analyst can
significantly improve the utility achieved by querying the API while satisfying
the same privacy bounds.
