# General-Purpose f-DP Estimation and Auditing in a Black-Box Setting

arXiv: https://arxiv.org/abs/2502.07066 · HTML: https://mlsanity.com/paper/2502.07066/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing](https://mlsanity.com/paper/2506.16666/) (2025)
- [Sequential Auditing for f-Differential Privacy](https://mlsanity.com/paper/2602.06518/) (2026)
- [When Are Trade-Off Functions Testable from Finite Samples?](https://mlsanity.com/paper/2605.10774/) (2026)
- [Let's Ask Gauss: Improved One-Run Privacy Auditing](https://mlsanity.com/paper/2606.12733/) (2026)

## Abstract (arXiv)

In this paper we propose new methods to statistically assess f-Differential Privacy (f-DP), a recent refinement of differential privacy (DP) that remedies certain weaknesses of standard DP (including tightness under algorithmic composition). A challenge when deploying differentially private mechanisms is that DP is hard to validate, especially in the black-box setting. This has led to numerous empirical methods for auditing standard DP, while f-DP remains less explored. We introduce new black-box methods for f-DP that, unlike existing approaches for this privacy notion, do not require prior knowledge of the investigated algorithm. Our procedure yields a complete estimate of the f-DP trade-off curve, with theoretical guarantees of convergence. Additionally, we propose an efficient auditing method that empirically detects f-DP violations with statistical certainty, merging techniques from non-parametric estimation and optimal classification theory. Through experiments on a range of DP mechanisms, we demonstrate the effectiveness of our estimation and auditing procedures.