English

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing

Cryptography and Security 2025-12-15 v3 Machine Learning

Abstract

In this paper, we systematize research on auditing Differential Privacy (DP) techniques, aiming to identify key insights and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata that DP audits should target -- namely, efficiency, end-to-end-ness, and tightness. Then, we systematize the modes of operation of state-of-the-art DP auditing techniques, including threat models, attacks, and evaluation functions. This allows us to highlight key details overlooked by prior work, analyze the limiting factors to achieving the three desiderata, and identify open research problems. Overall, our work provides a reusable and systematic methodology geared to assess progress in the field and identify friction points and future directions for our community to focus on.

Keywords

Cite

@article{arxiv.2506.16666,
  title  = {The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing},
  author = {Meenatchi Sundaram Muthu Selva Annamalai and Borja Balle and Jamie Hayes and Georgios Kaissis and Emiliano De Cristofaro},
  journal= {arXiv preprint arXiv:2506.16666},
  year   = {2025}
}

Comments

Published in the Proceedings of the 4th IEEE Secure and Trustworthy Machine Learning Conference (IEEE SaTML 2026). Please cite the SaTML version