Privacy in Statistical Databases

1. Front Matter

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2. Tabular Data Protection

   1. Enabling Statistical Analysis of Suppressed Tabular Data

      • Lawrence H. Cox

      Pages 1-10

   2. Assessing the Information Loss of Controlled Adjustment Methods in Two-Way Tables

      • Jordi Castro, José A. González

      Pages 11-23

   3. Further Developments with Perturbation Techniques to Protect Tabular Data

      • María-Salomé Hernández-García, Juan-José Salazar-González

      Pages 24-35

   4. Comparison of Different Sensitivity Rules for Tabular Data and Presenting a New Rule – The Interval Rule

      • Johan Bring, Qun Wang

      Pages 36-47

   5. Pre-tabular Perturbation with Controlled Tabular Adjustment: Some Considerations

      • Sarah Giessing

      Pages 48-61

   6. Measuring Disclosure Risk with Entropy in Population Based Frequency Tables

      • Laszlo Antal, Natalie Shlomo, Mark Elliot

      Pages 62-78

   7. A CTA Model Based on the Huber Function

      • Jordi Castro

      Pages 79-88

3. Microdata Masking

   1. Density Approximant Based on Noise Multiplied Data

      • Yan-Xia Lin

      Pages 89-104

   2. Reverse Mapping to Preserve the Marginal Distributions of Attributes in Masked Microdata

      • Krishnamurty Muralidhar, Rathindra Sarathy, Josep Domingo-Ferrer

      Pages 105-116

   3. JPEG-Based Microdata Protection

      • Javier Jiménez, Guillermo Navarro-Arribas, Vicenç Torra

      Pages 117-129

4. Protection Using Privacy Models

   1. Improving the Utility of Differential Privacy via Univariate Microaggregation

      • David Sánchez, Josep Domingo-Ferrer, Sergio Martínez

      Pages 130-142

   2. Differentially Private Exponential Random Graphs

      • Vishesh Karwa, Aleksandra B. Slavković, Pavel Krivitsky

      Pages 143-155

   3. k ^m-Anonymity for Continuous Data Using Dynamic Hierarchies

      • Olga Gkountouna, Sotiris Angeli, Athanasios Zigomitros, Manolis Terrovitis, Yannis Vassiliou

      Pages 156-169

   4. Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases

      • Fei Yu, Michal Rybar, Caroline Uhler, Stephen E. Fienberg

      Pages 170-184

5. Synthetic Data

   1. Disclosure Risk Evaluation for Fully Synthetic Categorical Data

      • Jingchen Hu, Jerome P. Reiter, Quanli Wang

      Pages 185-199

   2. v-Dispersed Synthetic Data Based on a Mixture Model with Constraints

      • Anna Oganian

      Pages 200-212

   3. Nonparametric Generation of Synthetic Data for Small Geographic Areas

      • Joseph W. Sakshaug, Trivellore E. Raghunathan

      Pages 213-231

   4. Using Partially Synthetic Data to Replace Suppression in the Business Dynamics Statistics: Early Results

      • Javier Miranda, Lars Vilhuber

      Pages 232-242

   5. Synthetic Longitudinal Business Databases for International Comparisons

      • Jörg Drechsler, Lars Vilhuber

      Pages 243-252

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2014, held in Ibiza, Spain in September 2014 under the sponsorship of the UNESCO chair in Data Privacy. The 27 revised full papers presented were carefully reviewed and selected from 41 submissions. The scope of the conference is on following topics: tabular data protection, microdata masking, protection using privacy models, synthetic data, record linkage, remote access, privacy-preserving protocols, and case studies.

• data anonymization and sanitization
• database and storage security
• database privacy
• database protection
• human and societal aspects of security and privacy
• information accountability and usage control
• privacy protections
• pseudonymity, anonymity and untraceability
• security and privacy
• security services
• statistical databases

• Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Tarragona, Catalonia

  Josep Domingo-Ferrer

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