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 privacy-preserving data publishing mechanisms and introduces the concept of differential privacy to protect individual data while providing accurate statistics.
Explores differential privacy, hypothesis testing, and composition of mechanisms, discussing optimal privacy regions and privacy degradation under repeated accesses.
