Optimizing Hierarchical Queries for the Attribution Reporting API
Data Structures and Algorithms
2023-11-28 v2 Cryptography and Security
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.
Keywords
Cite
@article{arxiv.2308.13510,
title = {Optimizing Hierarchical Queries for the Attribution Reporting API},
author = {Matthew Dawson and Badih Ghazi and Pritish Kamath and Kapil Kumar and Ravi Kumar and Bo Luan and Pasin Manurangsi and Nishanth Mundru and Harikesh Nair and Adam Sealfon and Shengyu Zhu},
journal= {arXiv preprint arXiv:2308.13510},
year = {2023}
}
Comments
Appeared at AdKDD 2023 workshop; Final proceedings version