Overview of differential privacy (DP), a mathematical framework for protecting individual data within larger datasets. They trace its origins from the failures of naive anonymization to its formal definition and core mechanisms like the Laplace and Gaussian noise methods.

The text highlights Differentially Private Stochastic Gradient Descent (DP-SGD) as the primary algorithm for implementing DP in machine learning (ML), discussing its hyperparameters and supporting software libraries like TensorFlow Privacy and PyTorch Opacus.

Furthermore, the sources explore the inherent privacy-utility-fairness trade-off in DP, analyze its evaluation metrics, compare it to other Privacy-Enhancing Technologies (PETs) such as Federated Learning (FL) and Homomorphic Encryption (HE), and examine its real-world applications in entities like the U.S. Census Bureau and Apple.

Finally, the sources look to the future of DP, particularly its application to Large Language Models (LLMs) and its crucial role in regulatory compliance and ethical AI.