Differential Privacy
Explains how differential privacy prevents AI models from leaking sensitive information by adding calibrated noise to aggregates. Covers the privacy loss parameter epsilon and the trade-off between privacy and predictive accuracy, with examples showing how small datasets can expose individual records.
