What does "anonymization" of data achieve?

Anonymization of data is a practice that removes personal identifiers from a dataset, ensuring that individuals cannot be identified from that data. This process involves altering data in such a way that it can no longer be linked back to specific individuals, thus achieving privacy and compliance with various data protection regulations. By removing or modifying elements such as names, addresses, and other unique identifiers, anonymization helps organizations mitigate the risks associated with data breaches and unauthorized access to personal information.

The options related to data transmission, usability for analysis, and encryption do not capture the core purpose of anonymization. While protecting data during transmission is important, it is not an aspect of anonymization. Additionally, anonymization does not render data unusable for analysis; rather, it allows for the analysis of trends and patterns without compromising individual privacy. Lastly, encryption is a different process aimed at securing data by encoding it, whereas anonymization is focused on removing identifiable information altogether.