Explores challenges in deep learning and machine learning applications, covering surveillance, privacy, manipulation, fairness, interpretability, energy efficiency, cost, and generalization.
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.
Introduces the K-Norm Gradient Mechanism (KNG) for achieving differential privacy with practical examples and insights on its advantages over existing mechanisms.
Explores the intersection of machine learning and privacy, discussing confidentiality, attacks, differential privacy, and trade-offs in federated learning.
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 privacy-preserving data publishing mechanisms, including k-anonymity and differential privacy, and their practical applications and challenges.
Explores the challenges of protecting location privacy and various techniques to mitigate location-related inferences, highlighting the importance of trust assumptions and practical issues.
