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