# Differential Privacy: An Estimation Theory-Based Method for Choosing Epsilon

arXiv: https://arxiv.org/abs/1510.00917 · HTML: https://mlsanity.com/paper/1510.00917/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2015.

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

- [How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application](https://mlsanity.com/paper/2209.02022/) (2022)
- [Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM](https://mlsanity.com/paper/2206.03488/) (2022)

## Abstract (arXiv)

Differential privacy is achieved by the introduction of Laplacian noise in the response to a query, establishing a precise trade-off between the level of differential privacy and the accuracy of the database response (via the amount of noise introduced). However, the amount of noise to add is typically defined through the scale parameter of the Laplace distribution, whose use may not be so intuitive. In this paper we propose to use two parameters instead, related to the notion of interval estimation, which provide a more intuitive picture of how precisely the true output of a counting query may be gauged from the noise-polluted one (hence, how much the individual's privacy is protected).