English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/130619
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Data Privacy with R

AutorAbril, Daniel; Navarro-Arribas, Guillermo; Torra, Vicenç
Palabras claveDisclosure risk
Data privacy
Privacy Preserving Data Mining
Fecha de publicación2015
CitaciónAdvanced Research in Data Privacy, Studies in Computational Intelligence, vol. 567: 63-82 (2015).
ResumenPrivacy Preserving Data Mining (PPDM) is an application field, which is becoming very relevant. Its goal is the study of new mechanisms which allow the dissemination of confidential data for data mining tasks while preserving individual private information. Additionally, due to the relevance of R language in the statistics and data mining communities, it is undoubtedly a good environment to research, develop and test privacy techniques aimed to data mining. In this chapter we outline some helpful tools in R to introduce readers to that field, so that we present several PPDM protection techniques as well as their information loss and disclosure risk evaluation process and outline some tools in R to help to introduce practitioners to this field. © Springer International Publishing Switzerland 2015.
Identificadoresdoi: 10.1007/978-3-319-09885-2_5
issn: 1860949X
Aparece en las colecciones: (IIIA) Libros y partes de libros
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Mostrar el registro completo

NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.