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Volume 16 Issue 1

k-Anonymity: From Theory to Applications

Sabrina De Capitani di Vimercati^(a), Sara Foresti^(a), Giovanni Livraga^(a), Pierangela Samarati^(a),(*)

Transactions on Data Privacy 16:1 (2023) 25 - 49

Abstract, PDF

(a) Computer Science Department, Università degli Studi di Milano, Italy.

e-mail:firstname.lastname @unimi.it; firstname.lastname @unimi.it; firstname.lastname @unimi.it; firstname.lastname @unimi.it

Abstract

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.

^* Corresponding author.

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