Preserving Privacy in On-Line Analytical Processing (Olap)

Lingyu Wang

ISBN 10: 0387462732 / ISBN 13: 9780387462738

Verlag: Springer Us Nov 2006, 2006

Beschreibung

Neuware - 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.

Bestandsnummer des Verkäufers 9780387462738

Bibliografische Details

• Titel: Preserving Privacy in On-Line Analytical Processing (Olap)
• Verlag: Springer Us Nov 2006
• Erscheinungsjahr: 2006
• Sprache: Englisch
• ISBN-10: 0387462732
• ISBN-13: 9780387462738
• Einband: Buch
• Zustand: Neu
• Abmessungen: 242B x 161 H x 16 T Millimeter Breite: 242 mm, Höhe: 161 mm, Tiefe: 16 mm
• Gewicht: 445 Gramm

Über diesen Titel

Inhaltsangabe

This book 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.

Von der hinteren Coverseite

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.