Differential Privacy

Differential Privacy:

Differential Privacy is a characteristic of a privacy preserving algorithm – it is a guarantee that no one can learn anything significant about any individual from their inclusion in the data processed by that algorithm. It’s a strong way to protect privacy of aggregate statistics – such as counts and averages. Differentially private statistics are engineered such that the statistic will be similar, regardless of whether a particular user is included in the data. Typically, a system achieves differential privacy by restricting the statistics that are released and adding random noise to the statistics.

Differential privacy has a parameter called epsilon, which controls the level of privacy. So long as epsilon is set appropriately, differential privacy is one of the strongest privacy guarantees available for practical use.​

Return to glossary

Share this post

Ready to learn more about Privitar?

Our team of data privacy experts is here to answer your questions and discuss how data privacy can fuel your business.