Lecture
This lecture introduces the K-Norm Gradient Mechanism (KNG) as a novel approach to achieving differential privacy. The instructor discusses motivating examples for privacy, the concept of differential privacy, and the Exponential Mechanism. The lecture covers the K-Norm Gradient Mechanism in detail, explaining its advantages over existing mechanisms and its applications in privacy-preserving data analysis. Various examples, including Laplace Mechanism, Exponential Mechanism, and Objective Perturbation, are presented to illustrate the practical implementation of KNG. The lecture concludes with insights on privacy, utility, and the connections between KNG and other privacy mechanisms.