anonimitizálás

Anonimitizálás is the process of removing or modifying personally identifiable information from data so that it can no longer be used to identify an individual. This is often done to protect privacy, particularly when data is shared or used for research purposes. The goal is to render the data effectively anonymous, meaning that even if combined with other information, it would be extremely difficult to link back to a specific person.

There are several techniques used in anonymization. These include generalization, where specific values are replaced with broader categories (e.g., replacing an exact age with an age range), suppression, where certain data points are removed entirely, and perturbation, where noise is added to the data to obscure individual values. Pseudonymization is a related but distinct process where identifiers are replaced with artificial ones, allowing for re-identification if the key is available.

Anonymized data can be valuable for statistical analysis, machine learning, and other data-driven applications, as it allows for the study of trends and patterns without compromising individual privacy. However, complete anonymization can be challenging, and there is always a risk of re-identification, especially when dealing with complex datasets or external information. Regulations like GDPR emphasize the importance of anonymization and set standards for its effective implementation.