Lecture
This lecture covers the concepts of differential privacy, explaining why it is possible compared to traditional anonymization methods. It delves into the composability of differential privacy, privacy budget management, post-processing immunity, Laplacian distribution, parameter selection, and mechanisms to achieve differential privacy. The exponential mechanism and randomized response techniques are also discussed, along with their differential privacy guarantees and real-world applications in data publishing.