Which term describes removing personal identifiers from a dataset to protect privacy?

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Multiple Choice

Which term describes removing personal identifiers from a dataset to protect privacy?

Removing personal identifiers from a dataset to protect privacy is data anonymization. Anonymization transforms data so individuals cannot be identified directly or indirectly, often by removing, generalizing, or aggregating identifiers (like names, exact addresses, or unique IDs) and by reducing detail (for example, using age ranges instead of exact ages). This approach aims to prevent re-identification even when additional information is available. Related techniques exist, but they don’t guarantee the same level of privacy in a shared dataset. De-identification involves removing identifiers but may still allow re-linkage with external data. Masking hides parts of data but can sometimes be reversed. Encryption protects data in storage or transit but isn’t about making a dataset safe to share for analysis once decrypted. In short, anonymization is the best-fit term for making a dataset privacy-safe for sharing by eliminating the ability to identify individuals.

Removing personal identifiers from a dataset to protect privacy is data anonymization. Anonymization transforms data so individuals cannot be identified directly or indirectly, often by removing, generalizing, or aggregating identifiers (like names, exact addresses, or unique IDs) and by reducing detail (for example, using age ranges instead of exact ages). This approach aims to prevent re-identification even when additional information is available.

Related techniques exist, but they don’t guarantee the same level of privacy in a shared dataset. De-identification involves removing identifiers but may still allow re-linkage with external data. Masking hides parts of data but can sometimes be reversed. Encryption protects data in storage or transit but isn’t about making a dataset safe to share for analysis once decrypted. In short, anonymization is the best-fit term for making a dataset privacy-safe for sharing by eliminating the ability to identify individuals.

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