Final answer:
K-anonymity is a data anonymization technique that helps protect individual identities to a certain extent but does not guarantee protection against all types of privacy violations, such as homogeneity and background knowledge attacks.
Step-by-step explanation:
False, k-anonymity does not protect users from all privacy violations. The concept of k-anonymity is a technique used in data anonymization to protect individual identities within a dataset. A dataset is said to have k-anonymity if each data instance cannot be distinguished from at least k-1 other instances based on certain identifying attributes. However, k-anonymity has several limitations. It does not protect against attacks such as homogeneity and background knowledge attacks. If all individuals in a k-anonymous dataset share the same sensitive attribute value or an attacker has additional information, privacy breaches can still occur despite k-anonymity.