Preserving Privacy in On-Line Analytical Processing (OLAP)

By Lingyu Wang , Sushil Jajodia , Duminda Wijesekera

Preserving Privacy in On-Line Analytical Processing (OLAP) Cover Image

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.

Full Description

  • ISBN13: 978-0-3874-6273-8
  • 196 Pages
  • User Level: Professionals
  • Publication Date: April 6, 2007
  • Available eBook Formats: PDF
  • eBook Price: $129.00
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Full Description
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.
Table of Contents

Table of Contents

  1. Preface.
  2. Introduction.
  3. OLAP and Data Cubes.
  4. Inference Control in Statistical Databases.
  5. Inferences in Data Cubes.
  6. Cardinality
  7. Based Inference Control.
  8. Parity
  9. Based Inference Control for Range Queries.
  10. Lattice
  11. Based Inference Control in Data Cubes.
  12. Query
  13. Driven Inference Control in Data Cubes.
  14. Conclusion and Future Direction.
  15. References.
  16. Index.
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