Anonymization

What is Anonymization?

Anonymization is the process of transforming data so that individuals can no longer reasonably be identified, directly or indirectly, from the resulting dataset.

Why it matters

True anonymization aims to make re-identification impractical even when the data is combined with other available information. Techniques can include aggregation, generalization, suppression, and noise addition. Because re-identification risk depends on the data, context, and available auxiliary information, anonymization should be assessed rather than assumed. Unlike pseudonymized data, properly anonymized data is generally not intended to be reversible.

Example

Instead of storing exact ages, postcodes, and timestamps, a dataset may use age bands, broad regions, and rounded dates so records cannot be linked back to specific people.