Which practice involves combining deidentified data with other data sources to re-link with individuals?

Which practice involves combining deidentified data with other data sources to re-link with individuals?

• A. Tokenization
• B. Data Scrubbing
• C. Reidentification
• D. Alert

Answer: (C)

Reidentification is the process of linking data that has been deidentified with additional data sources to reveal the identities behind the records. Even after direct identifiers are removed, datasets often contain quasi-identifiers—like age, ZIP code, or gender—that, when combined with other data, can be used to pinpoint who the data belongs to. Tokenization replaces sensitive values with non-identifying tokens and is designed to prevent easy re-linking; it’s about protecting data, not enabling it. Data scrubbing removes or masks PII to reduce exposure before sharing, rather than enabling re-linking. An alert is simply a notification mechanism and not a data-privacy technique. The described practice, reidentification, highlights why combining datasets can undermine anonymity and why robust de-identification and privacy-preserving methods are important.

Reidentification is the process of linking data that has been deidentified with additional data sources to reveal the identities behind the records. Even after direct identifiers are removed, datasets often contain quasi-identifiers—like age, ZIP code, or gender—that, when combined with other data, can be used to pinpoint who the data belongs to. Tokenization replaces sensitive values with non-identifying tokens and is designed to prevent easy re-linking; it’s about protecting data, not enabling it. Data scrubbing removes or masks PII to reduce exposure before sharing, rather than enabling re-linking. An alert is simply a notification mechanism and not a data-privacy technique. The described practice, reidentification, highlights why combining datasets can undermine anonymity and why robust de-identification and privacy-preserving methods are important.