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The technique that adds calibrated noise to model gradients during training to provide privacy guarantees is called ________-SGD.
Answer: Differential Privacy / DP
DP-SGD adds noise to gradients during training, providing mathematical privacy guarantees. Critical for training ML models on sensitive data while preventing membership inference.