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AI & Machine Learning · Advanced

Differential Privacy

A mathematical privacy guarantee that limits how much any individual's data can affect a model's or query's output.

Definition

Differential privacy adds calibrated noise, controlled by a privacy budget epsilon, so that results are nearly the same whether or not any one person's data is included. It protects against membership inference and is used in training and analytics.

Exam tip

Smaller epsilon means stronger privacy and more noise.

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