Anonymiserats

Anonymiserats is a Swedish term used in data protection to refer to data or information that has been anonymized, meaning personal identifiers have been removed or transformed to prevent the identification of individuals. It is commonly applied in research, statistics and reporting where the release of insights should not expose private information.

The anonymisation process typically includes identifying direct identifiers (such as names, national identity numbers, addresses) and removing or substituting them with non-identifying values. Additional steps may include generalization (coarse-graining dates or locations), data masking, pseudonymization, aggregation, or the introduction of controlled statistical noise. In more rigorous contexts, techniques such as k-anonymity, l-diversity, or differential privacy may be used to reduce re-identification risk.

Applications include medical research datasets, publicly released mobility or sales data, and administrative statistics. When performed effectively, anonymiserats data enables analysis and reporting without linking back to individuals.

Limitations and legal considerations: Anonymisation is not foolproof; re-identification can occur, especially when data are combined with external sources. Therefore governance, risk assessments, and compliance with data protection laws (such as the EU's GDPR) are essential. Data that is not properly anonymised may still be subject to privacy protections.