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Differential Privacy

Privacy

Data Minimization Data Aggregation Privacy Budget Anonymization

Formal privacy framework limiting the influence of any single record on outputs.

Definition

Differential privacy provides a mathematical guarantee that an analysis output changes only slightly if any one individual’s data is added or removed, typically by adding calibrated randomness.

In plain English Formal privacy framework limiting the influence of any single record on outputs.

Why this matters

Why it matters: It helps measure and control privacy loss when releasing aggregate insights.

Example

Example: Query system adds calibrated noise to counts and enforces limits on repeated queries.