Machine Learning and Privacy

This lecture explores the trade-offs of Differential Privacy (DP) mechanisms, discussing how they partially destroy information but promise to cancel out noise with enough data. It delves into the impact of DP on accuracy across subgroups, the need for big data in DP, and the interaction between machine learning and privacy. The lecture also covers machine learning-based privacy attacks, including membership inference and stylometry attacks.