Explores the challenges of protecting location privacy and various techniques to mitigate location-related inferences, highlighting the importance of trust assumptions and practical issues.
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 privacy technologies, emphasizing the importance of protecting communication layers and discussing anti-surveillance PETS and privacy properties.
