Autor segons l'article: Zigomitros A; Casino F; Solanas A; Patsakis C
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Casino Cembellín, Francisco José / Solanas Gómez, Agustín
Paraules clau: Utility Statistics Sociology Smart city Sequential publication Review Publishing Protection Privacy preserving data publishing Privacy Preserving privacy Microdata Microaggregation Licenses K-anonymity Differential privacy Data protection Data privacy Data anonymization Anonymizing classification data
Resum: © 2013 IEEE. Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including $k$ -anonymity, $\ell $ -diversity, $t$ -closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.
Àrees temàtiques: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 2169-3536
Adreça de correu electrònic de l'autor: franciscojose.casino@urv.cat agusti.solanas@urv.cat
Identificador de l'autor: 0000-0003-4296-2876 0000-0002-4881-6215
Data d'alta del registre: 2023-02-19
Volum de revista: 8
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://ieeexplore.ieee.org/document/9032138
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Ieee Access. 8 51071-51099
Referència de l'ítem segons les normes APA: Zigomitros A; Casino F; Solanas A; Patsakis C (2020). A Survey on Privacy Properties for Data Publishing of Relational Data. Ieee Access, 8(), 51071-51099. DOI: 10.1109/ACCESS.2020.2980235
DOI de l'article: 10.1109/ACCESS.2020.2980235
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications
Repositori URV