Privacy-preserving data publishing: Synthetic Data Generation
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Explores the multifaceted nature of privacy, its definitions, implications, and regulatory frameworks, focusing on the real-name web phenomenon exemplified by Facebook's name policy.
Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Delves into the legal aspects of real-time bidding in online advertising, focusing on data protection laws, consent management challenges, and recent legal implications.
Introduces the K-Norm Gradient Mechanism (KNG) for achieving differential privacy with practical examples and insights on its advantages over existing mechanisms.
