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Preserving Privacy in On-Line Analytical Processing (OLAP)

  • Book
  • © 2007

Overview

Authors:
  1. Lingyu Wang

    1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada

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    You can also search for this author in PubMed   Google Scholar

  2. Sushil Jajodia

    1. George Mason University, Fairfax

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    You can also search for this author in PubMed   Google Scholar

  3. Duminda Wijesekera

    1. George Mason University, Fairfax

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    You can also search for this author in PubMed   Google Scholar

  • First book that concentrates solely on OLAP systems
  • Includes Lattice-Based Inference Control Method
  • Discusses methods that can be implemented on the basis of \emph(three-Tier) Inference control model in OLAP systems
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (9 chapters)

  1. Front Matter

    Pages I-XI
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  2. Introduction

    Pages 1-11

  3. OLAP and Data Cubes

    Pages 13-19

  4. Inference Control in Statistical Databases

    Pages 21-35

  5. Inferences in Data Cubes

    Pages 37-51

  6. Cardinality-based Inference Control

    Pages 53-89

  7. Parity-based Inference Control for Range Queries

    Pages 91-117

  8. Lattice-based Inference Control in Data Cubes

    Pages 119-145

  9. Query-driven Inference Control in Data Cubes

    Pages 147-167

  10. Conclusion and Future Direction

    Pages 169-171

  11. Back Matter

    Pages 173-180
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Keywords

About this book

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.

 

Authors and Affiliations

  • Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada

    Lingyu Wang

  • George Mason University, Fairfax

    Sushil Jajodia, Duminda Wijesekera

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