k-Anonymity, l-Diversity, t-Closeness

Apply formal anonymisation models to real datasets. Learn k-anonymity (and homogeneity/background-knowledge attacks), l-diversity (and skewness/similarity attacks), t-closeness, the practical bounds for utility-preserving k, and the workflow for choosing the right model per release.