English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/162167
COMPARTIR / IMPACTO:
Estadísticas
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:
Título

Privacy in data mining

AutorDomingo-Ferrer, Joan; Torra, Vicenç
Palabras clavePrivacy-preserving data mining (PPDM)
Data qualityK-anonymity
Confidentiality
Data privacy
Data mining
Fecha de publicación2005
EditorSpringer
CitaciónData Mining and Knowledge Discovery 11 (2): 117- 119 (2005)
ResumenAuthors views on combining computer science and statistics to foster the development of privacy-preserving data mining (PPDM) are described. In the first paper authors determined which PPDM techniques are best to protect sensitive information, and how the, quality and privacy measures must be defined. The second paper analyzes the problem of confidentiality in categorical statistical databases when association rules are to be preserved. The third paper proposes to use probabilities to define bounded information loss measures for any statistic of interest. The fourth paper deals with k-anonymity, which is a useful concept to manage the conflict between data quality and individual privacy.
URIhttp://hdl.handle.net/10261/162167
Identificadoresdoi: 10.1007/s10618-005-0009-3
issn: 1384-5810
Aparece en las colecciones: (IIIA) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 

Artículos relacionados:


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