Inference Control in Statistical Databases

1. Front Matter

   Pages I-VIII

   PDF

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

3. Tabular Data Protection

   1. Cell Suppression: Experience and Theory

      • Dale A. Robertson, Richard Ethier

      Pages 8-20

   2. Bounds on Entries in 3-Dimensional Contingency Tables Subject to Given Marginal Totals

      • Lawrence H. Cox

      Pages 21-33

   3. Extending Cell Suppression to Protect Tabular Data against Several Attackers

      • Juan José Salazar González

      Pages 34-58

   4. Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions

      • Jordi Castro

      Pages 59-73

   5. HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables

      • Peter-Paul de Wolf

      Pages 74-82

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

   PDF

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.

• computer
• computer science
• data analysis
• data mining
• database
• knowledge discovery
• privacy
• statistics

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

  Josep Domingo-Ferrer

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