Overview
Data Anonymization is a type of information sanitization whose intent is privacy protection. It involves the permanent removal or encryption of personal identifiers that connect an individual to their data.
Techniques
- Generalization: Replacing specific values with broader categories (e.g., replacing an exact age with an age range).
- Suppression: Removing certain data fields entirely.
- Noise Addition: Adding random data to a dataset to prevent individual identification.
Irreversibility
True anonymization must be irreversible. If the data can be 're-identified' by combining it with other datasets, it is not truly anonymous.