Lingyu Wang, Sushil Jajodia, Duminda Wijesekera

Preserving Privacy in On-Line Analytical Processing (OLAP)

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Advances in Information Security 29
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4419-4278-4
• Softcover reprint of hardcover 1st ed. 2007
• Seitenzahl: 192
• Erscheinungstermin: 19. November 2010
• Englisch
• Abmessung: 235mm x 155mm x 11mm
• Gewicht: 299g
• ISBN-13: 9781441942784
• ISBN-10: 1441942785
• Artikelnr.: 32135001

Autorenporträt

Addresses the privacy issue of On-Line Analytic Processing systems
Details how to keep the performance overhead of these security methods at a reasonable level
Examines how a balance between security, availability, and performance can feasibly be achieved in OLAP systems

Inhaltsangabe

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.

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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.