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Preserving Privacy in On-Line Analytical Processing (OLAP)

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This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. OLAP systems usually need to meet two confli... Weiterlesen
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Beschreibung

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.



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.

 



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: 9780387462745
ISBN: 978-0-387-46274-5
Digitaler Kopierschutz: Wasserzeichen
Format: E-Book (pdf)
Hersteller: Springer US
Herausgeber: Springer
Genre: IT & Internet
Anzahl Seiten: 180
Veröffentlichung: 06.04.2007
Jahr: 2007
Dateigrösse: 8.7 MB

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