anonimkan

Anonimkan is the Indonesian term for the process of making data or information anonymous, removing or obfuscating identifiers to prevent attribution to individuals. In privacy and data protection contexts, anonymization refers to transforming data so that an individual cannot be identified, directly or indirectly, even when combined with other data.

When applied to datasets, anonymization typically involves removing or replacing personal identifiers (names, addresses, ID numbers) and altering or aggregating quasi-identifiers (birth year, gender, ZIP code) to reduce re-identification risk. Common techniques include suppression, generalization, masking, hashing, tokenization, data aggregation, and adding statistical noise. Differential privacy is a modern approach that adds controlled noise to queries to protect individual records.

Anonimkan is distinguished from pseudonymization, where identifiers are replaced with tokens but the linkage to the original identity remains possible if additional information is available; anonymization aims to be irreversible.

Legal and ethical considerations: many data protection regimes treat anonymized data as outside the scope of personal data regulation when true anonymization is achieved. However, achieving true anonymization can be challenging, and there is always residual re-identification risk, especially when combined with external datasets or advanced analytics.

Applications and risks: anonymized data enables research, statistics, and public sharing while protecting privacy, but inappropriate methods can leave individuals identifiable. Therefore, anonymization should be accompanied by a risk assessment, documenting methods, and ongoing review as data and technologies evolve.