Machine Learning and Privacy

This lecture explores the intersection between machine learning and privacy, discussing topics such as data collection, model training, deployment, attacks on private data, responsible use of private data, and the implications of machine learning models on privacy. It delves into the concept of membership inference, attribute inference, overfitting, threshold attacks, and the vulnerability of models to privacy breaches, providing insights on how adversaries can exploit machine learning models to infer sensitive information.