Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/138201
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | An evolutionary approach to enhance data privacy |
Autor: | Jimenez, Javier; Marés, Jordi; Torra, Vicenç CSIC ORCID | Palabras clave: | Data privacy Disclosure risk Risk assessment Information privacy and security Evolutionary algorithms |
Fecha de publicación: | 2011 | Editor: | Springer Nature | Citación: | Soft Computing 15: 1301- 1311 (2011) | Resumen: | Dissemination of data with sensitive information about individuals has an implicit risk of unauthorized disclosure. Perturbative masking methods propose the distortion of the original data sets before publication, tackling a difficult tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). In this paper, we describe how information loss and disclosure risk measures can be integrated within an evolutionary algorithm to seek new and enhanced masking protections for continuous microdata. The proposed technique constitutes a hybrid approach that combines state-of-the-art protection methods with an evolutionary algorithm optimization. We also provide experimental results using three data sets in order to illustrate and empirically evaluate the application of this technique. © 2010 Springer-Verlag. | URI: | http://hdl.handle.net/10261/138201 | DOI: | 10.1007/s00500-010-0672-1 | Identificadores: | doi: 10.1007/s00500-010-0672-1 issn: 1432-7643 |
Aparece en las colecciones: | (IIIA) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
accesoRestringido.pdf | 15,38 kB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
9
checked on 26-mar-2024
WEB OF SCIENCETM
Citations
6
checked on 24-feb-2024
Page view(s)
156
checked on 19-abr-2024
Download(s)
82
checked on 19-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.