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.
Introduces the K-Norm Gradient Mechanism (KNG) for achieving differential privacy with practical examples and insights on its advantages over existing mechanisms.
Explores challenges in deep learning and machine learning applications, covering surveillance, privacy, manipulation, fairness, interpretability, energy efficiency, cost, and generalization.
