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Preserving Privacy in On-Line Analytical Processing (OLAP) de Lingyu Wang

Preserving Privacy in On-Line Analytical Processing (OLAP): Advances in Information Security, cartea 29

Autor Lingyu Wang, Sushil Jajodia, Duminda Wijesekera
en Limba Engleză Hardback – 14 noi 2006
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.
 
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Specificații

ISBN-13: 9780387462738
ISBN-10: 0387462732
Pagini: 180
Ilustrații: XII, 180 p. 20 illus.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.45 kg
Ediția:2007
Editura: Springer Us
Colecția Springer
Seria Advances in Information Security

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

OLAP and Data Cubes.- Inference Control in Statistical Databases.- Inferences in Data Cubes.- Cardinality-based Inference Control.- Parity-based Inference Control for Range Queries.- Lattice-based Inference Control in Data Cubes.- Query-driven Inference Control in Data Cubes.- Conclusion and Future Direction.

Textul de pe ultima copertă

On-Line Analytic Processing (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. Existing inference control methods in statistical databases usually exhibit high performance overhead and limited effectiveness when applied to OLAP systems.
Preserving Privacy in On-Line Analytical Processing reviews a series of methods that can precisely answer data cube-style OLAP queries regarding sensitive data while provably preventing adversaries from inferring the data. How to keep the performance overhead of these security methods at a reasonable level is also addressed. Achieving a balance between security, availability, and performance is shown to be feasible in OLAP systems.
Preserving Privacy in On-Line Analytical Processing is designed for the professional market, composed of practitioners and researchers in industry.  This book is also appropriate for graduate-level students in computer science and engineering.
 

Caracteristici

First book that concentrates solely on OLAP systems Includes Lattice-Based Inference Control Method Discusses methods that can be implemented on the basis of \emph(three-Tier) Inference control model in OLAP systems Includes supplementary material: sn.pub/extras