What Is Differential Privacy?
Differential Privacy is a mathematical privacy guarantee that limits the amount of information that can be inferred about any individual from an AI model or dataset, by adding calibrated statistical noise.
Differential Privacy — a mathematical privacy guarantee that limits the amount of information that can be inferred about any individual from an AI model or dataset, by adding calibrated statistical noise.
Differential privacy (DP) is a rigorous formal definition of privacy that can be proven mathematically. A DP mechanism guarantees that the probability of any output changes by at most a factor of e^ε when any single individual's data is added or removed — ε (epsilon) is the privacy budget. Lower epsilon means stronger privacy but typically worse model utility. DP is used in practice by Google (Chrome RAPPOR), Apple (iOS telemetry), Meta (ad measurement), and the US Census Bureau. It is increasingly referenced as a technical control in AI governance frameworks.
Source: Dwork & Roth (2014); NIST SP 800-226
Plain-language explanation
Differential privacy (DP) is a rigorous formal definition of privacy that can be proven mathematically. A DP mechanism guarantees that the probability of any output changes by at most a factor of e^ε when any single individual's data is added or removed — ε (epsilon) is the privacy budget. Lower epsilon means stronger privacy but typically worse model utility. DP is used in practice by Google (Chrome RAPPOR), Apple (iOS telemetry), Meta (ad measurement), and the US Census Bureau. It is increasingly referenced as a technical control in AI governance frameworks.
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