anonymisera

Anonymisera is a term used in data protection and privacy contexts to describe the process of removing or altering personal identifiers from data so that individuals cannot be identified. The concept encompasses both complete anonymization, where re-identification is not reasonably possible, and partial forms such as pseudonymization or masking that reduce identifiability while preserving some data utility.

Common methods associated with anonymization include removing direct identifiers (names, addresses, social security numbers), replacing them with pseudonyms, applying data masking, and using techniques like generalization, suppression, or data synthesis. More advanced approaches aim to provide formal privacy guarantees, such as differential privacy, or to ensure data satisfy criteria like k-anonymity, l-diversity, or t-closeness, which reduce the risk of linking records to individuals.

Anonymization is widely used in research, statistics, healthcare, government data releases, and journalism to balance the value of data with individual privacy. It is influenced by legal frameworks such as data protection laws, which encourage or require minimization and, in some cases, mandate documented anonymization practices. Importantly, true anonymization aims to render re-identification impractical, but experts warn that certain combinations of data and external information can still threaten anonymity, so risk assessments and governance are critical components of the process.

See also: anonymization, pseudonymization, differential privacy.