# Empirical Differential Privacy

arXiv: https://arxiv.org/abs/1910.12820 · HTML: https://mlsanity.com/paper/1910.12820/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [PSY: Posterior Sampling Based Privacy Enhancer in Large Language Models](https://mlsanity.com/paper/2410.18824/) (2024)
- [PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning](https://mlsanity.com/paper/2510.23427/) (2025)

## Abstract (arXiv)

We show how to achieve differential privacy with no or reduced added noise, based on the empirical noise in the data itself. Unlike previous works on noiseless privacy, the empirical viewpoint avoids making any explicit assumptions about the random process generating the data.