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Covers privacy mechanisms, their pros and cons, and their application in various scenarios, emphasizing privacy as a security property and its significance in society.
Explores challenges in deep learning and machine learning applications, covering surveillance, privacy, manipulation, fairness, interpretability, energy efficiency, cost, and generalization.
Explores privacy-preserving data publishing mechanisms, including k-anonymity and differential privacy, and their practical applications and challenges.
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.
