Which term describes combining deidentified data sets with other data sources to reveal identities?

• A. Data Masking
• B. Unencrypted Data (Cleartext/Plaintext)
• C. Data Scrubbing
• D. Reidentification

Answer: (D)

Reidentification is the act of tying deidentified data back to real people by linking it with other data sources. Even when direct identifiers are removed, unique combinations of non-identifying details—like age, ZIP code, or gender—can be matched with external datasets to reveal who the data represents. That exposure risk is why privacy protections must consider potential cross-dataset linkages, not just masking or cleaning within a single file.

Data masking involves hiding or obfuscating actual values to protect sensitive information, which is about protecting data within a dataset rather than identifying someone by combining sources. Unencrypted data refers to information stored in plaintext, which is a security vulnerability but not the process of reidentifying someone. Data scrubbing is the practice of cleaning data to improve quality, such as removing duplicates or correcting errors, and it doesn’t describe the act of linking datasets to discover identities.