Privacy in Statistical Databases

1. Front Matter

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2. Tabular Data Protection

   1. Privacy Disclosure Analysis and Control for 2D Contingency Tables Containing Inaccurate Data

      • Bing Liang, Kevin Chiew, Yingjiu Li, Yanjiang Yang

      Pages 1-16

   2. A Tool for Analyzing and Fixing Infeasible RCTA Instances

      • Jordi Castro, José A. González

      Pages 17-28

   3. Branch-and-Cut versus Cut-and-Branch Algorithms for Cell Suppression

      • Juan-José Salazar-González

      Pages 29-40

   4. Data Swapping for Protecting Census Tables

      • Natalie Shlomo, Caroline Tudor, Paul Groom

      Pages 41-51

   5. Eliminating Small Cells from Census Counts Tables: Some Considerations on Transition Probabilities

      • Sarah Giessing, Jörg Höhne

      Pages 52-65

   6. Three Ways to Deal with a Set of Linked SBS Tables Using τ-argus

      • Peter-Paul de Wolf, Anco Hundepool

      Pages 66-73

3. Microdata Protection

   1. IPUMS-International Statistical Disclosure Controls: 159 Census Microdata Samples in Dissemination, 100+ in Preparation

      • Robert McCaa, Steven Ruggles, Matt Sobek

      Pages 74-84

   2. Uncertainty for Anonymity and 2-Dimensional Range Query Distortion

      • Spyros Sioutas, Emmanouil Magkos, Ioannis Karydis, Vassilios S. Verykios

      Pages 85-96

   3. PRAM Optimization Using an Evolutionary Algorithm

      • Jordi Marés, Vicenç Torra

      Pages 97-106

   4. Multiplicative Noise Protocols

      • Anna Oganian

      Pages 107-117

   5. Measurement Error and Statistical Disclosure Control

      • Natalie Shlomo

      Pages 118-126

   6. Semantic Microaggregation for the Anonymization of Query Logs

      • Arnau Erola, Jordi Castellà-Roca, Guillermo Navarro-Arribas, Vicenç Torra

      Pages 127-137

   7. Data Environment Analysis and the Key Variable Mapping System

      • Mark Elliot, Susan Lomax, Elaine Mackey, Kingsley Purdam

      Pages 138-147

4. Synthetic Data

   1. Using Support Vector Machines for Generating Synthetic Datasets

      • Jörg Drechsler

      Pages 148-161

   2. Synthetic Data for Small Area Estimation

      • Joseph W. Sakshaug, Trivellore E. Raghunathan

      Pages 162-173

   3. Disclosure Risk of Synthetic Population Data with Application in the Case of EU-SILC

      • Matthias Templ, Andreas Alfons

      Pages 174-186

5. Differential Privacy

   1. Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables

      • Stephen E. Fienberg, Alessandro Rinaldo, Xiaolin Yang

      Pages 187-199

   2. Does Differential Privacy Protect Terry Gross’ Privacy?

      • Krish Muralidhar, Rathindra Sarathy

      Pages 200-209

   3. Some Additional Insights on Applying Differential Privacy for Numeric Data

      • Rathindra Sarathy, Krish Muralidhar

      Pages 210-219

Privacy in statistical databases is a discipline whose purpose is to provide so- tionstothetensionbetweenthesocial,political,economicandcorporatedemand for accurate information, and the legal and ethical obligation to protect the p- vacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data o- ers (those organizations spending money in data collection) and the users (the ones querying the database or the search engine, who would like their queries to stay con?dential). Beyond law and ethics, there are also practical reasons for data-collecting agencies and corporations to invest in respondent privacy: if individual respondents feel their privacy guaranteed, they are likely to provide moreaccurateresponses. Data ownerprivacyis primarilymotivatedbypractical considerations: if an enterprise collects data at its own expense, it may wish to minimize leakage of those data to other enterprises (even to those with whom joint data exploitation is planned). Finally, user privacy results in increaseduser satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy,bothofwhich started in the 1970s: the ?rst one stems from o?cial statistics, where the dis- pline is also known as statistical disclosure control (SDC), and the second one originates from computer science and database technology. In o?cial statistics, the basic concern is respondent privacy. In computer science, the initial mo- vation was also respondent privacy but, from 2000 onwards, growing attention has been devoted to owner privacy (privacy-preserving data mining) and user privacy (private informationretrieval).

• algorithm analysis
• data mining
• database
• microdata protection
• privacy
• tabular data protection
• data structures

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

  Josep Domingo-Ferrer

• Department of Informatics, Ionian University, Kerkyra, Greece

  Emmanouil Magkos

Policies and ethics