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Preserving Privacy in On-Line Analytical Processing (OLAP)

  • Kartonierter Einband
  • 192 Seiten
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This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. It reviews a series of methods that can preci... Weiterlesen
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Beschreibung

This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. It 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 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.



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



Autorentext
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

Klappentext

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.

 



Zusammenfassung

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.

 



Inhalt
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.

Produktinformationen

Titel: Preserving Privacy in On-Line Analytical Processing (OLAP)
Autor:
EAN: 9781441942784
ISBN: 1441942785
Format: Kartonierter Einband
Herausgeber: Springer US
Genre: Informatik
Anzahl Seiten: 192
Gewicht: 300g
Größe: H235mm x B155mm x T10mm
Jahr: 2010
Auflage: Softcover reprint of hardcover 1st ed. 2007

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