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Explores differential privacy, hypothesis testing, and composition of mechanisms, discussing optimal privacy regions and privacy degradation under repeated accesses.
Explores the intersection of machine learning and privacy, discussing confidentiality, attacks, differential privacy, and trade-offs in federated learning.
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.
Discusses the ongoing challenge of embedded security in healthcare, emphasizing the need for solutions to protect against malicious behavior and secure critical assets and services.