Explores the intersection of machine learning and privacy, discussing confidentiality, attacks, differential privacy, and trade-offs in federated learning.
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.
