Explores Federated Machine Learning and Differential Privacy in Machine Learning, discussing attacks, defenses, and challenges.
Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Explores machine learning fundamentals and privacy risks, focusing on privacy attacks and collaborative machine learning.
Introduces a course on Privacy Enhancing Technologies covering various mechanisms and emphasizing privacy as a security property.
Explores the sensitivity of location data, inference of points of interest, and techniques for protecting location privacy.
