How does differential privacy protect analytics results?

Differential privacy is a mathematical method for producing useful statistics while limiting what those results reveal about any individual’s contribution. It typically adds carefully controlled random variation, known as noise, so the results remain broadly similar whether or not one person’s data is included. This helps preserve overall trends while making individual contributions harder to distinguish.

For example, a website could use a differentially private calculation to report approximately how many visitors used a feature, rather than publishing an exact count. The added noise helps protect individual activity while keeping the result useful for understanding feature usage.

Three implementation choices matter:

• The unit being protected, such as a person rather than a single event.

• The privacy parameters, which control the strength of the guarantee.

• The cumulative information disclosed through repeated analyses.

Adding arbitrary noise is not enough to establish differential privacy. The mechanism and its privacy accounting must support the claimed guarantee.

The ICO’s differential privacy guidance explains these trade-offs. It can support privacy-preserving analytics, although original records retained behind the analysis may still be personal data.