Introduces the K-Norm Gradient Mechanism (KNG) for achieving differential privacy with practical examples and insights on its advantages over existing mechanisms.
Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Explores data privacy challenges and perspectives in eHealth research, focusing on GDPR compliance, sensitive health data management, and decentralized agents.
