In data handling, what does "anonymization" mean?

Anonymization refers to the process of removing personally identifiable information (PII) from data sets, making it impossible to identify individuals from the remaining information. This technique is essential for protecting individual privacy while still allowing data to be used for analysis and research purposes. By stripping away direct identifiers such as names, addresses, and social security numbers, data can be shared and analyzed without risking unwanted disclosure of personal information.

This approach plays a crucial role in various fields, including healthcare and social sciences, where maintaining patient confidentiality and participant anonymity is paramount. Anonymized data can still yield valuable insights while adhering to ethical guidelines and legal standards related to data protection. Thus, the option about removing personally identifiable information is correct, as it directly captures the essence of what anonymization entails within the context of data handling.