Anonymisation
Processing personal data so that individuals are no longer identifiable by any means reasonably likely to be used, taking account of other data that might be available. Effectively anonymised data falls outside UK GDPR, but the threshold is high.
Why it matters
Anonymised data can be shared and analysed far more freely, which makes it attractive for research, benchmarking and model development. The risk is that data believed to be anonymous can often be re-identified by combining quasi-identifiers such as postcode, age and dates.
Anonymisation should be treated as an assessed outcome rather than a technique. Aggregation, generalisation, noise and suppression all help, but whether the result is anonymous depends on context and must be reviewed as circumstances change.
In practice
For example, a UK transport operator sharing journey data with a university might publish counts by station and hour rather than individual journeys, suppress small counts and remove exact timestamps, then document why re-identification is not reasonably likely.
Where Rodan fits
Rodan helps teams choose between anonymisation, pseudonymisation and synthetic data for analytics and AI work in Analytics and Intelligence engagements.