Lecture
This lecture delves into the second part of privacy-preserving data publishing, focusing on mechanisms to share sensitive data while protecting privacy. Topics covered include k-anonymity, t-closeness, synthetic data, and differential privacy. The instructor explains differential privacy concepts, such as input and output perturbation, and practical applications in companies like Apple and Google. The lecture also highlights the challenges and pitfalls of differential privacy, such as unbounded sensitivity and disparate impact on subpopulations.