Which statement best describes de-identification?

Which statement best describes de-identification?

• A. Removing identifiers to protect privacy.
• B. Encrypting data for security.
• C. Sharing with all colleagues.
• D. Aggregating data for analysis.

Answer: (A)

De-identification means removing identifiers from a dataset so individuals cannot be identified. This involves taking out direct identifiers like names, addresses, and social security numbers, and may include masking or coding other information that could link back to a person when combined with other data. The goal is to protect privacy while still allowing data to be used for analysis. This is the best description because it focuses on making the data non-identifiable by removing the identifiers themselves. Encrypting data protects information in storage or transit, but the data can still contain identifiers and could become identifiable if decrypted or accessed with the key. Sharing with all colleagues increases privacy risks rather than safeguarding them. Aggregating data can help privacy by summarizing information, but de-identification specifically refers to removing or obscuring identifiers in the data itself.

De-identification means removing identifiers from a dataset so individuals cannot be identified. This involves taking out direct identifiers like names, addresses, and social security numbers, and may include masking or coding other information that could link back to a person when combined with other data. The goal is to protect privacy while still allowing data to be used for analysis.

This is the best description because it focuses on making the data non-identifiable by removing the identifiers themselves. Encrypting data protects information in storage or transit, but the data can still contain identifiers and could become identifiable if decrypted or accessed with the key. Sharing with all colleagues increases privacy risks rather than safeguarding them. Aggregating data can help privacy by summarizing information, but de-identification specifically refers to removing or obscuring identifiers in the data itself.