Covers the principles and strategies of privacy engineering, emphasizing the importance of embedding privacy into IT systems and the challenges faced in achieving privacy by design.
Explores privacy-preserving data publishing mechanisms, including k-anonymity and differential privacy, and their practical applications 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.
