Anonymization

Anonymization is the process of removing or altering personal data so individuals cannot be identified from the data, alone or in combination with other information. The aim is to protect privacy while allowing data to be used for analysis, research, or reporting.

Techniques include data masking, generalization, suppression, and adding statistical noise. Formal approaches such as k-anonymity, l-diversity, and t-closeness seek to prevent re-identification in partially synthetic datasets. Differential privacy adds random noise to outputs to limit what any single record can reveal.

Anonymization differs from pseudonymization and de-identification. Pseudonymization replaces identifiers with substitutes but can be reversible if the linkage is known; anonymization seeks irreversible removal of identifiers in practice.

Applications span healthcare, public health, research, transportation, and business analytics. Data custodians weigh privacy risks against data utility and may adopt context-specific risk assessments.

Risks remain: advances in auxiliary information and data linkage can enable re-identification. The effectiveness of anonymization is context-dependent and may require ongoing evaluation. Regulations such as privacy laws encourage or require adequate anonymization to enable data sharing while protecting individuals.

In practice, anonymization is a balance between minimizing disclosure risk and preserving analytical usefulness, with techniques evolving as new threats and data sources emerge.