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Preserving Privacy in On-Line Analytical Processing (OLAP)

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 dat...

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Bibliographic Details
Main Authors: Wang, Lingyu, Jajodia, Sushil (Author), Wijesekera, Duminda (Author)
Format: eBook
Language:English
Published: New York, NY Springer US 2007, 2007
Edition:1st ed. 2007
Series:Advances in Information Security
Subjects:
Computer Communication Networks
Cryptography
Data Structures And Information Theory
Database Management
Application Software
Computer Networks 
Information Theory
Data Encryption (computer Science)
Data Structures (computer Science)
Cryptology
Computer And Information Systems Applications
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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520 |a 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. 

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