Differential Privacy

nounverified·updated May 18, 2026

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Differential privacy is a method for measuring how much information the output of a computation reveals about an individual. It is based on the randomised injection of "noise". Noise is a random alteration of data in a dataset so that values such as direct or indirect identifiers of individuals are harder to reveal. An important aspect of differential privacy is the concept of “epsilon” or ɛ, which determines the level of added noise. Epsilon is also known as the “privacy budget” or “privacy parameter”.

MWE

Classifications

per-axis with provenance

Entity Type

Control85%llm-generatedllm:claude-haiku-4-5

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Sensitivity

—60%llm-generatedllm:claude-haiku-4-5

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Information Class

Pii75%llm-generatedllm:claude-haiku-4-5

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Variants

3 surface forms

plural: Differential Privacies
possessive: Differential Privacy's
pluralpossessive: Differential Privacies'

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