# Statistical Quantification of Differential Privacy: A Local Approach

arXiv: https://arxiv.org/abs/2108.09528 · HTML: https://mlsanity.com/paper/2108.09528/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Unleashing the Power of Randomization in Auditing Differentially Private ML](https://mlsanity.com/paper/2305.18447/) (2023)
- [The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing](https://mlsanity.com/paper/2506.16666/) (2025)
- [Weights Shuffling for Improving DPSGD in Transformer-based Models](https://mlsanity.com/paper/2407.15414/) (2024)

## Abstract (arXiv)

In this work, we introduce a new approach for statistical quantification of differential privacy in a black box setting. We present estimators and confidence intervals for the optimal privacy parameter of a randomized algorithm A, as well as other key variables (such as the "data-centric privacy level"). Our estimators are based on a local characterization of privacy and in contrast to the related literature avoid the process of "event selection" - a major obstacle to privacy validation. This makes our methods easy to implement and user-friendly. We show fast convergence rates of the estimators and asymptotic validity of the confidence intervals. An experimental study of various algorithms confirms the efficacy of our approach.