A data analyst removes personally identifying information from a dataset. What task are they performing?

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Multiple Choice

A data analyst removes personally identifying information from a dataset. What task are they performing?

Data anonymization is the process of removing personally identifying information from a dataset so individuals can’t be identified. This often involves deleting direct identifiers (like names or social security numbers) and generalizing or suppressing indirect identifiers (such as exact ages or precise addresses) to prevent linking records back to a person. This is different from data masking, which hides values but can sometimes be reversible; data encryption, which makes data unreadable without a key but doesn’t remove identifiers from the dataset itself; and data aggregation, which summarizes data and reduces detail but doesn’t specifically remove PII. So removing personally identifiable information fits anonymization, ensuring the data can be analyzed or shared without exposing who the data belongs to.

Data anonymization is the process of removing personally identifying information from a dataset so individuals can’t be identified. This often involves deleting direct identifiers (like names or social security numbers) and generalizing or suppressing indirect identifiers (such as exact ages or precise addresses) to prevent linking records back to a person. This is different from data masking, which hides values but can sometimes be reversible; data encryption, which makes data unreadable without a key but doesn’t remove identifiers from the dataset itself; and data aggregation, which summarizes data and reduces detail but doesn’t specifically remove PII. So removing personally identifiable information fits anonymization, ensuring the data can be analyzed or shared without exposing who the data belongs to.

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