# Optimizing Hierarchical Queries for the Attribution Reporting API

arXiv: https://arxiv.org/abs/2308.13510 · HTML: https://mlsanity.com/paper/2308.13510/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2023.

## Built on by (most cited first, 3 of 3)

- [Denoising the US Census: Succinct Block Hierarchical Regression](https://mlsanity.com/paper/2603.10099/) (2026)
- [Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees](https://mlsanity.com/paper/2605.22756/) (2026)
- [Fixed-Parameter Tractability of Private Synthetic Data Generation](https://mlsanity.com/paper/2606.11283/) (2026)

## Abstract (arXiv)

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.