anonymize

Anonymize is the process of modifying data so that individuals cannot be identified from it. In privacy and data protection, anonymization removes or alters personal identifiers so the data cannot be linked to a specific person, even when combined with other information. It is distinct from pseudonymization, where identifiers are replaced with substitutes but can be re-linked.

Techniques range from simple masking and redaction to data generalization, suppression, and perturbation. More formal approaches include k-anonymity, l-diversity, and t-closeness; differential privacy adds random noise to protect individual contributions. The choice of technique reflects a trade-off between privacy and data utility.

Anonymization is used for research data, statistics, medical records, and public datasets, as well as redacting documents and metadata in emails, photos, and location data. Examples include replacing exact birth dates with years, aggregating ages into ranges, removing names, and stripping IP addresses.

Risks of re-identification remain, especially when anonymized data are combined with other sources. Legal regimes such as GDPR treat truly anonymized data as outside the scope of personal data rules; however, practical anonymization requires assessment of residual risk. Ethical considerations emphasize transparency, consent, and responsible data use.