Learn advanced privacy concepts and techniques in this comprehensive tutorial delivered by Adam Smith from Boston University and Lydia Zakynthinou from Johns Hopkins University as part of the Federated and Collaborative Learning Boot Camp. Explore sophisticated privacy-preserving methods, differential privacy mechanisms, and their applications in federated learning environments. Delve into theoretical foundations and practical implementations of privacy protection in distributed machine learning systems. Examine how privacy constraints impact collaborative learning algorithms and discover strategies for maintaining data confidentiality while enabling effective model training across multiple parties. Gain insights into the latest research developments in privacy-preserving technologies and their real-world applications in federated learning scenarios.