k-Anonymity: From Theory to Applications

k-Anonymity is a well-known privacy model originally designed to protect the identities of the individuals involved in the release of a data collection. It provides a privacy requirement and a metric able to capture the protection degree enjoyed by respondents (i.e., the individuals to whom released data refer). Since its proposal, k-anonymity has been heavily investigated, with works addressing extensions of its privacy requirement to capture specific privacy risks, approaches to efficiently enforce k-anonymity, and adaptations to application scenarios that go beyond the publication of a dataset. In this paper, we illustrate k-anonymity and its main extensions. We also discuss some of the main approaches proposed for the enforcement of the corresponding privacy requirements, and some advanced application scenarios.