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.

k-Anonymity: From Theory to Applications / S. De Capitani di Vimercati, S. Foresti, G. Livraga, P. Samarati. - In: TRANSACTIONS ON DATA PRIVACY. - ISSN 1888-5063. - 16:1(2023), pp. 25-49.

k-Anonymity: From Theory to Applications

S. De Capitani di Vimercati
Primo
;
S. Foresti
Secondo
;
G. Livraga
Penultimo
;
P. Samarati
Ultimo
2023

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.
k-Anonymity; l-Diversity; Privacy; Quasi-identifier; Generalization; Fragmentation; Microaggregation
Settore INF/01 - Informatica
   Multi-Owner data Sharing for Analytics and Integration respecting Confidentiality and Owner control (MOSAICrOWN)
   MOSAICrOWN
   EUROPEAN COMMISSION
   H2020
   825333

   Machine Learning-based, Networking and Computing Infrastructure Resource Management of 5G and beyond Intelligent Networks (MARSAL)
   MARSAL
   EUROPEAN COMMISSION
   H2020
   101017171

   High quality Open data Publishing and Enrichment (HOPE)
   HOPE
   MINISTERO DELL'ISTRUZIONE E DEL MERITO
   2017MMJJRE_003
2023
http://www.tdp.cat/issues21/tdp.a460a22.pdf
Article (author)
File in questo prodotto:
File Dimensione Formato  
tdp.a460a22.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 284.42 kB
Formato Adobe PDF
284.42 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/954133
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact