Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Explores the multifaceted nature of privacy, its definitions, implications, and regulatory frameworks, focusing on the real-name web phenomenon exemplified by Facebook's name policy.
Explores the legal aspects of data protection law, covering historical developments, international instruments, GDPR provisions, and fundamental rights.
Covers the principles and strategies of privacy engineering, emphasizing the importance of embedding privacy into IT systems and the challenges faced in achieving privacy by design.
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 machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Explores challenges in privacy-preserving data publishing, including failed de-identification examples and privacy threats, and presents a case study on Airbnb's efforts to address racist practices while protecting user privacy.
Explores the intersection of machine learning and privacy, discussing confidentiality, attacks, differential privacy, and trade-offs in federated learning.
