The correct option is Inject random noise into analytic outputs so aggregated metrics reflect the dataset yet vary unpredictably.

This approach means adding carefully calibrated random noise to query answers so results show accurate aggregates while limiting any one person from changing the output in a detectable way. Differential privacy creates a mathematical bound on how much influence an individual's data can have on published summaries and that bound is controlled by a privacy parameter often called epsilon. The method trades a small amount of accuracy for a strong privacy guarantee and it works across repeated analyses when a privacy budget is managed.

Homomorphic encryption is incorrect because it allows computation on encrypted data without revealing raw values but it does not itself provide the statistical privacy guarantee that differential privacy gives when publishing aggregated outputs.

Substitute numeric entries with their column average for analysis is incorrect because simple averaging or masking can still leak information and it does not provide a formal privacy guarantee or a controllable privacy loss parameter like differential privacy.

Google Cloud Data Loss Prevention is incorrect because that is a tool for discovering and redacting sensitive data and it is not the definition of differential privacy. It is also a product specific to Google Cloud and not the privacy mechanism described by the correct choice.

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