A mathematical framework for releasing aggregate information about a dataset while limiting what can be inferred about any individual within it, usually by adding calibrated statistical noise to results. Unlike De-identification, which removes identifiers, differential privacy provides a quantifiable bound on individual disclosure risk. It is used by statistical agencies and increasingly in health research where individual-level data cannot be shared.
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