Optimizing Hierarchical Queries for the Attribution Reporting API

TL;DR

This paper develops algorithms to improve hierarchical ad conversion queries using the Attribution Reporting API by denoising data, ensuring consistency, and optimizing privacy budgets, with experimental validation.

Contribution

It introduces novel algorithms that combine optimization and differential privacy techniques to enhance hierarchical query accuracy under privacy constraints.

Findings

Algorithms effectively denoise API outputs.

Methods ensure consistency across hierarchical levels.

Optimized privacy budgets improve data utility.

Abstract

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.