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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.
Explores privacy technologies, emphasizing the importance of protecting communication layers and discussing anti-surveillance PETS and privacy properties.
Explores the historical development and legal aspects of data protection laws, international instruments, challenges of online tracking, legal bases for processing personal data, and privacy regulations.
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 homomorphic encryption, its applications, challenges, and comparisons with Trusted Execution Environments, as well as the use of aggregated keys for secure data sharing.
Explores the SwissCovid app, a proximity-tracing solution against COVID-19, covering technical aspects, building trust, privacy, security, and collaboration with Google and Apple.
Explores asymmetric cryptography basics, including encryption, signatures, and Diffie-Hellman, along with advanced topics like RSA and quantum computing implications.
