Which term refers to the removal or modification of personally identifiable information from a data set?

The correct term that refers to the removal or modification of personally identifiable information (PII) from a data set is anonymization. This process involves altering the data in such a way that individuals cannot be re-identified or linked to their information. Anonymization is crucial in data privacy and protection practices, as it helps organizations to use data for analysis and research without compromising an individual's privacy.

For example, when a data set is anonymized, specific identifiers such as names, addresses, and social security numbers are either removed completely or replaced with pseudonyms, ensuring that the data cannot be traced back to the individuals it originally pertained to. This is particularly important in compliance with regulations such as GDPR or HIPAA, which emphasize the safeguarding of personal information.

Other terms mentioned relate to different aspects of data handling: re-identification is the process of matching anonymized data with the original identity, tokenization substitutes sensitive data with non-sensitive equivalents, and Data Loss Prevention (DLP) encompasses strategies used to prevent unauthorized access or sharing of sensitive information. Anonymization specifically focuses on the aspect of safeguarding individual identities within datasets, making it the most accurate choice in this context.