differential privacy

Privacy starts as a governance requirement and turns into a training-time technique. What lands is knowing what differential privacy actually promises (and what it costs), plus how federated learning and DP fit together. Here is the rigorous, honest answer.

Privacy in ML is a toolbox, not one switch, and each tool defends a different threat. The signal is mapping DP, federated learning, confidential computing, encryption, and minimization to what each one actually protects, and knowing they compose.

Large models memorize and can be coaxed into emitting training examples verbatim, a genuine privacy and copyright liability. The defenses reach across data, training, and output. Here is the layered answer.

Epsilon is the privacy knob, and shipping DP means picking a number, justifying it, and tracking what each query spends. The signal is treating epsilon as a finite budget that composes, not a magic constant. Here is the answer.

Stripping direct identifiers protects nobody, because the identifying information was never in the name column. Here is how a linkage attack actually works, why k-anonymity and its patches keep failing, and the one defense that makes a promise it can keep.

Picking epsilon is the easy half. The hard half is the accuracy you just gave up, and the levers that get it back are not the ones most candidates reach for.