Differential Privacy: Privacy Guarantees and Mechanisms

This lecture introduces differential privacy, a concept in randomized algorithms ensuring statistical insignificance in results between neighboring databases. The instructor explains the strength of differential privacy in preventing inference about individual records and presents the Laplace Mechanism as a tool to achieve it. The lecture covers the global noise sensitivity, Laplace mechanism-based algorithms, and the implications of differential privacy in algorithm design. It concludes by discussing the tradeoff between privacy and accuracy, emphasizing the pessimistic nature of differential privacy.