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The technique that adds calibrated noise to model outputs to prevent membership inference is called ________ Privacy.
Answer: Differential
Differential Privacy mathematically guarantees that model outputs don't reveal whether specific individuals were in training data. Critical for privacy-preserving ML deployment.