anonimleme

Anonimleme is the process of removing or modifying personal identifiers in data to prevent the re-identification of individuals. In Turkish usage, the term combines anonim (anonymous) with the suffix -leme, forming a noun that denotes the act of anonymizing. It is a central concept in data protection and privacy when data containing personal information is prepared for sharing or analysis.

Techniques used in anonimleme range from direct removal of identifiers (names, addresses, social security numbers) to indirect methods such as generalization or binning (replacing exact ages or dates with ranges), data masking, and the introduction of random noise. More formal approaches include k-anonymity, l-diversity, t-closeness, and differential privacy, which aim to balance privacy with data utility and analytical usefulness. Generating synthetic data is another strategy that preserves overall patterns without exposing real individuals.

Challenges of anonimleme include the risk of re-identification when anonymized data are combined with external datasets or auxiliary information. There is a fundamental trade-off between privacy protection and data utility: stronger privacy often reduces the usefulness of the data for analysis. Assessments and risk mitigation strategies are essential, especially in sensitive domains such as health, finance, and social research.

Applications of anonimleme span statistical agencies, academic research, and industry collaborations where data sharing is necessary but personal data protection is a priority. Legal frameworks often encourage or require anonymization as a means to minimize privacy risks while enabling secondary use of data.