Data Privacy: K-anonymity

This lecture covers the concept of k-anonymity in data privacy, focusing on techniques to prevent the identification of individuals in published datasets by ensuring that each individual is indistinguishable from at least k-1 others. The instructor explains how quasi-identifiers and generalization are used to achieve k-anonymity, discusses attacks on k-anonymity such as unsorted matching and complementary release attacks, and presents practical algorithms like Datafly for k-anonymization. The lecture emphasizes the importance of robust privacy guarantees while highlighting the vulnerabilities of k-anonymity to various attacks.