Differential privacy adds calibrated noise during training to mathematically guarantee training examples can't be extracted. Core guarantee: Model output is statistically similar whether any individual example is in training data or not - bounded privacy leakage (ε, δ parameters). Mechanism (DP-SGD): Clip individual gradients (bound influence), add Gaussian noise to aggregated gradients, privacy amplification through subsampling. Privacy budget (ε): Lower ε = stronger privacy, but more noise = lower accuracy. Typical values: 1-10. Trade-offs: Privacy vs utility - more privacy requires more noise, degrades model quality. Need large datasets to overcome noise. For LLMs: DP-SGD during training, DP fine-tuning of pretrained models, inference-time DP for queries. Advantages: Mathematically provable guarantee, composes across multiple analyses, standardized framework. Limitations: Accuracy degradation, computational overhead, privacy budget accounting complexity, may not protect all types of information. Tools: Opacus (PyTorch), TensorFlow Privacy. Regulations: Increasingly viewed as gold standard for privacy compliance in ML.