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：我们研究了基于广告转化测量《归因报告API》的摘要报告执行层级查询的任务。研究表明，优化方法和差分隐私技术有助于应对API中隐私保护机制引入的噪声。具体而言，我们提出了以下算法：(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，以及 (ii) 优化树结构不同层级间的隐私预算分配。我们在公开数据集上对提出的算法进行了实验评估。