Preserving Privacy in On-Line Analytical Processing (Olap) - (Advances in Information Security) (Hardcover)
About this item
Highlights
- 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
- Author(s): Lingyu Wang & Sushil Jajodia & Duminda Wijesekera
- 180 Pages
- Computers + Internet, Security
- Series Name: Advances in Information Security
Description
About the Book
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.
Book Synopsis
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
From the Back Cover
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.