# Data anonymization

> Changing a dataset so nobody in it can be identified, while keeping it useful.

- Level: Intermediate
- Topics: Data, Security
- URL: https://tidbit.legios.com.ar/data-anonymization/
- In Spanish: [Anonimización de datos](https://tidbit.legios.com.ar/es/data-anonymization/)

**Data anonymization** means transforming personal data so that the people behind it can no longer be identified, by anyone, with reasonable effort. A hospital that wants to share patient records with researchers, or a city that publishes taxi trips, has to do this first. Done right, the data still answers useful questions, such as how many people in their forties had a given illness, without pointing at anyone.

Deleting names is not enough. In a well-known 2000 study, Latanya Sweeney estimated that roughly 87% of people in the United States could be singled out by just ZIP code, gender and birth date. So real anonymization also **generalizes** values (an age range instead of a birth date, a region instead of a street), drops rare combinations, or adds noise, as differential privacy does.

It is easy to confuse with **pseudonymization**, which swaps names for codes but keeps a key somewhere to link them back. Under the GDPR, pseudonymized data is still personal data; only truly anonymous data falls outside the law. Several famous cases, like the Netflix Prize dataset in 2006, showed that data released as anonymous could be re-identified by matching it with other public information.

## An analogy

Like blurring faces in a crowd photo: you can still count people and see where they gathered, but if someone is wearing a very distinctive hat, the blur is not enough and you have to crop them out too.

## Keep in mind

- Removing names is not enough; combinations like ZIP, gender and birth date can identify people.
- Common techniques: removal, generalization, suppression of rare cases, added noise.
- Pseudonymized data can be linked back, so the GDPR still treats it as personal data.
- There is always a trade-off between privacy and how useful the data stays.

## Go deeper

- [Data anonymization on Wikipedia](https://en.wikipedia.org/wiki/Data_anonymization)
- [GDPR Recital 26: not applicable to anonymous data](https://gdpr-info.eu/recitals/no-26/)
- [Pseudonymization on Wikipedia](https://en.wikipedia.org/wiki/Pseudonymization)

## Related terms

- [Personal data and GDPR](https://tidbit.legios.com.ar/personal-data-and-gdpr/): Information about you, and the laws that say who can use it and how.
- [Regions and Availability Zones](https://tidbit.legios.com.ar/regions-and-availability-zones/): Where AWS physically lives: cities worldwide, each split into separate data centers.
- [Netflix](https://tidbit.legios.com.ar/netflix/): A streaming service, and the engineering it built to deliver video to the whole world.