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.

翻译：我们研究基于《归因报告API》摘要报告进行分层查询的任务，该API用于广告转化测量。我们证明，优化与差分隐私领域的方法有助于应对API中隐私保护机制引入的噪声。具体而言，我们提出了两类算法：（i）对API输出进行去噪处理，并确保树结构不同层级间的一致性；（ii）优化树结构不同层级的隐私预算分配。我们在公开数据集上对所提算法进行了实验评估。