Privacy-preserving Data Publishing: RAPPOR and Differential Privacy
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Introduces the K-Norm Gradient Mechanism (KNG) for achieving differential privacy with practical examples and insights on its advantages over existing mechanisms.
Explores privacy-preserving data publishing mechanisms and introduces the concept of differential privacy to protect individual data while providing accurate statistics.
Explores the historical development and legal aspects of data protection laws, international instruments, challenges of online tracking, legal bases for processing personal data, and privacy regulations.
Delves into the legal aspects of real-time bidding in online advertising, focusing on data protection laws, consent management challenges, and recent legal implications.
Explores challenges in deep learning and machine learning applications, covering surveillance, privacy, manipulation, fairness, interpretability, energy efficiency, cost, and generalization.
