Data Privacy: Techniques and Challenges

This lecture covers techniques for data privacy, including differential privacy and k-anonymity. It discusses concepts such as randomization guarantees, data perturbation, quasi-identifiers, attacks on k-anonymity, and differential privacy strength. The instructor explains how differential privacy ensures statistical insignificance for neighboring databases and the Laplace Mechanism as a tool to achieve it.