differential privacy

Privacy is a governance requirement that becomes a training-time technique. The signal is knowing what differential privacy actually guarantees (and its cost), and how federated learning and DP combine. Here is the rigorous, honest answer.

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

Large models memorize and can be prompted to emit training examples verbatim, a real privacy and copyright liability. The defenses span data, training, and output. Here is the layered answer.

Epsilon is the privacy knob, and shipping DP means choosing a number, defending it, and tracking what each query spends. The signal is treating epsilon as a finite budget with composition, not a magic constant. Here is the answer.