# Sequential Auditing for f-Differential Privacy

arXiv: https://arxiv.org/abs/2602.06518 · HTML: https://mlsanity.com/paper/2602.06518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Time-uniform, nonparametric, nonasymptotic confidence sequences](https://mlsanity.com/paper/1810.08240/) (2018)
- [Unlocking High-Accuracy Differentially Private Image Classification through Scale](https://mlsanity.com/paper/2204.13650/) (2022)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [Privacy Auditing with One (1) Training Run](https://mlsanity.com/paper/2305.08846/) (2023)
- [Geographic Spines in the 2020 Census Disclosure Avoidance System](https://mlsanity.com/paper/2203.16654/) (2022)
- [A Primer on Private Statistics](https://mlsanity.com/paper/2005.00010/) (2020)
- [Auditing f-Differential Privacy in One Run](https://mlsanity.com/paper/2410.22235/) (2024)
- [Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment](https://mlsanity.com/paper/2304.06929/) (2023)
- [PAC Privacy: Automatic Privacy Measurement and Control of Data Processing](https://mlsanity.com/paper/2210.03458/) (2022)
- [Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy](https://mlsanity.com/paper/2310.19973/) (2023)
- [CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples](https://mlsanity.com/paper/2008.07485/) (2020)
- [Sequentially Auditing Differential Privacy](https://mlsanity.com/paper/2509.07055/) (2025)
- [General-Purpose f-DP Estimation and Auditing in a Black-Box Setting](https://mlsanity.com/paper/2502.07066/) (2025)
- [Lower Bounds for R'enyi Differential Privacy in a Black-Box Setting](https://mlsanity.com/paper/2212.04739/) (2022)
- [Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance](https://mlsanity.com/paper/2308.08343/) (2023)

## Abstract (arXiv)

We present new auditors to assess Differential Privacy (DP) of an algorithm based on output samples. Such empirical auditors are common to check for algorithmic correctness and implementation bugs. Most existing auditors are batch-based or targeted toward the traditional notion of (varepsilon,δ)-DP; typically both. In this work, we shift the focus to the highly expressive privacy concept of f-DP, in which the entire privacy behavior is captured by a single tradeoff curve. Our auditors detect violations across the full privacy spectrum with statistical significance guarantees, which are supported by theory and simulations. Most importantly, and in contrast to prior work, our auditors do not require a user-specified sample size as an input. Rather, they adaptively determine a near-optimal number of samples needed to reach a decision, thereby avoiding the excessively large sample sizes common in many auditing studies. This reduction in sampling cost becomes especially beneficial for expensive training procedures such as DP-SGD. Our method supports both whitebox and blackbox settings and can also be executed in one-run frameworks.