Preserving Privacy in On-Line Analytical Processing (OLAP)

  • Lingyu Wang
  • Sushil Jajodia
  • Duminda Wijesekera

Part of the Advances in Information Security book series (ADIS, volume 29)

Table of contents

About this book


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.



Analytical Jajodia OLAP On-line Preserving Privacy Processing Wang Wijesekera data warehouse database

Authors and affiliations

  • Lingyu Wang
    • 1
  • Sushil Jajodia
    • 2
  • Duminda Wijesekera
    • 2
  1. 1.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada
  2. 2.George Mason UniversityFairfax

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