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Preserving Privacy in On-Line Analytical Processing (OLAP)

  • Fester Einband
  • 180 Seiten
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Preserving Privacy for On-Line Analytical Processing addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. O... Weiterlesen
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Beschreibung

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



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: 9780387462738
ISBN: 978-0-387-46273-8
Format: Fester Einband
Herausgeber: Springer, Berlin
Genre: Informatik
Anzahl Seiten: 180
Größe: H235mm x B235mm x T155mm
Jahr: 2006
Untertitel: Englisch
Auflage: 2007

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