Privacy Risks and Protections in Machine Learning Systems

Watch a 37-minute conference talk from the Toronto Machine Learning Series where Gautam Kamath, Assistant Professor at the University of Waterloo and Canada CIFAR AI Chair at Vector Institute, explores critical privacy concerns in machine learning systems. Discover how training data in ML models can potentially leak sensitive personal information and the associated risks this poses. Learn about robust methodologies and protective measures that can be implemented to safeguard user privacy, prevent vulnerabilities, and maintain trust in machine learning systems. Gain valuable insights into the intersection of privacy preservation and machine learning, essential knowledge for developers and practitioners working with sensitive data in AI applications.