Preserving Privacy in On-Line Analytical Processing (OLAP)

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Springer


Paru le : 2007-04-06

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Description
Preserving Privacy for On-Line Analytical Processing addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. OLAP systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. This volume reviews a series of methods that can precisely answer data cube-style OLAP, regarding sensitive data while provably preventing adversaries from inferring data. Preserving Privacy for On-Line Analytical Processing is appropriate for practitioners in industry as well as graduate-level students in computer science and engineering.  
Pages
180 pages
Collection
n.c
Parution
2007-04-06
Marque
Springer
EAN papier
9780387462738
EAN PDF
9780387462745

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
18
Taille du fichier
8724 Ko
Prix
95,39 €

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