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.
