# Differential privacy

URL: https://sellmydatatoai.net/glossary/differential-privacy

Differential privacy is a mathematical guarantee that limits how much any result reveals about whether a single person's data was included.

Differential privacy is a mathematical framework for measuring and limiting how much an individual's privacy is exposed when their data is included in a dataset or analysis. A process is differentially private if its output looks almost the same whether or not any one person's record is included, with the gap controlled by a privacy parameter usually written as epsilon.

## What guidance does NIST give?

NIST published [SP 800-226, Guidelines for Evaluating Differential Privacy Guarantees](https://csrc.nist.gov/pubs/sp/800/226/final), in March 2025 to help practitioners understand and evaluate software that claims to implement it.

## How is differential privacy used with training data?

Differential privacy is used to train models with noise added during training, to release statistics about a dataset, and to generate [synthetic data](https://sellmydatatoai.net/glossary/synthetic-data) with a measurable privacy bound. It reduces the risk that a model memorizes and repeats individual records.

It is not a substitute for [de-identification](https://sellmydatatoai.net/glossary/de-identification) of the raw data a seller delivers. A buyer that receives raw records gets no protection from differential privacy applied later, so sellers still need to remove identifiers before delivery.