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Inference Control in Statistical Databases

From Theory to Practice

  • Book
  • © 2002

Overview

Editors:
  1. Josep Domingo-Ferrer

    1. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2316)

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Table of contents (18 chapters)

  1. Front Matter

    Pages I-VIII
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  2. Advances in Inference Control in Statistical Databases: An Overview

    1. Advances in Inference Control in Statistical Databases: An Overview

      • Josep Domingo-Ferrer
      Pages 1-7

  4. Microdata Protection

    1. Model Based Disclosure Protection

      • Silvia Polettini, Luisa Franconi, Julian Stander
      Pages 83-96

    2. Microdata Protection through Noise Addition

      • Ruth Brand
      Pages 97-116

    3. Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique

      • Ramesh A. Dandekar, Michael Cohen, Nancy Kirkendall
      Pages 117-125

    4. Integrating File and Record Level Disclosure Risk Assessment

      • Mark Elliot
      Pages 126-134

    5. Disclosure Risk Assessment in Perturbative Microdata Protection

      • William E. Yancey, William E. Winkler, Robert H. Creecy
      Pages 135-152

    6. LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection

      • Ramesh A. Dandekar, Josep Domingo-Ferrer, Francesc Sebé
      Pages 153-162

    7. Post-Masking Optimization of the Tradeoff between Information Loss and Disclosure Risk in Masked Microdata Sets

      • Francesc Sebé, Josep Domingo-Ferrer, Josep Maria Mateo-Sanz, Vicenç Torra
      Pages 163-171

  5. Software and User Case Studies

    1. The CASC Project

      • Anco Hundepool
      Pages 172-180

    2. Tools and Strategies to Protect Multiple Tables with the GHQUAR Cell Suppression Engine

      • Sarah Giessing, Dietz Repsilber
      Pages 181-192

    3. SDC in the 2000 U.S. Decennial Census

      • Laura Zayatz
      Pages 193-202

    4. Applications of Statistical Disclosure Control at Statistics Netherlands

      • Eric Schulte Nordholt
      Pages 203-212

    5. Empirical Evidences on Protecting Population Uniqueness at Idescat

      • Julià Urrutia, Enric Ripoll
      Pages 213-229

  6. Back Matter

    Pages 230-230
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Keywords

About this book

Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source of data for producing statistics. Techniques used by intruders to make inferences compromising privacy increasingly draw on data mining, record linkage, knowledge discovery, and data analysis and thus statistical inference control becomes an integral part of computer science.
This coherent state-of-the-art survey presents some of the most recent work in the field. The papers presented together with an introduction are organized in topical sections on tabular data protection, microdata protection, and software and user case studies.

Editors and Affiliations

  • Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain

    Josep Domingo-Ferrer

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