Privacy in Statistical Databases: UNESCO Chair in Data Privacy International Conference, PSD 2008, Istanbul, Turkey, September 24-26, 2008, Proceedings (Lecture Notes in Computer Science, 5262)

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2008, held in September 2008 in Istanbul, Turkey, under the sponsorship of the UNESCO chair in Data Privacy. The 27 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on tabular data protection; microdata protection; online databases and remote access; privacy-preserving data mining and private information retrieval; and legal issues. Front Matter....Pages - Using a Mathematical Programming Modeling Language for Optimal CTA....Pages 1-12 A Data Quality and Data Confidentiality Assessment of Complementary Cell Suppression....Pages 13-23 Pre-processing Optimisation Applied to the Classical Integer Programming Model for Statistical Disclosure Control....Pages 24-36 How to Make the τ -ARGUS Modular Method Applicable to Linked Tables....Pages 37-49 Bayesian Assessment of Rounding-Based Disclosure Control....Pages 50-63 Cell Bounds in Two-Way Contingency Tables Based on Conditional Frequencies....Pages 64-76 Invariant Post-tabular Protection of Census Frequency Counts....Pages 77-89 A Practical Approach to Balancing Data Confidentiality and Research Needs: The NHIS Linked Mortality Files....Pages 90-99 From t -Closeness to PRAM and Noise Addition Via Information Theory....Pages 100-112 Robustification of Microdata Masking Methods and the Comparison with Existing Methods....Pages 113-126 A Preliminary Investigation of the Impact of Gaussian Versus t-Copula for Data Perturbation....Pages 127-138 Anonymisation of Panel Enterprise Microdata – Survey of a German Project....Pages 139-151 Towards a More Realistic Disclosure Risk Assessment....Pages 152-165 Assessing Disclosure Risk for Record Linkage....Pages 166-176 Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking....Pages 177-189 Parallelizing Record Linkage for Disclosure Risk Assessment....Pages 190-202 Extensions of the Re-identification Risk Measures Based on Log-Linear Models....Pages 203-212 Use of Auxiliary Information in Risk Estimation....Pages 213-226 Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification Disclosure Risks in Partially Synthetic Data....Pages 227-238 How Protective Are Synthetic Data?....Pages 239-246 Auditing Categorical SUM, MAX and MIN Queries....Pages 247-256 Reasoning under Uncertainty in On-Line Auditing....Pages 257-269 A Remote Analysis Server - What Does Regression Output Look Like?....Pages 270-283 Accuracy in Privacy-Preserving Data Mining Using the Paradigm of Cryptographic Elections....Pages 284-297 A Privacy-Preserving Framework for Integrating Person-Specific Databases....Pages 298-314 Peer-to-Peer Private Information Retrieval....Pages 315-323 Legal, Political and Methodological Issues in Confidentiality in the European Statistical System....Pages 324-334 Back Matter....Pages - Privacy in statistical databases is a discipline whose purpose is to provide solutions to the tension between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data owners (those organizations spending money in data collection) and the users (the ones querying the database, who would like their queries to stay con?d- tial). Beyond law and ethics, there are also practical reasons for data collecting agencies to invest in respondent privacy: if individual respondents feel their p- vacyguaranteed,they arelikelyto providemoreaccurateresponses. Data owner privacy is primarily motivated by practical considerations: if an enterprise c- lects 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 increased user satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy,bothofwhich started in the 1970s: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originatesfrom computer science and database technology. In o?cial statistics, the basic c- cern is respondent privacy.

this Book Constitutes The Refereed Proceedings Of The International Conference On Privacy In Statistical Databases, Psd 2008, Held In September 2008 In Istanbul, Turkey, Under The Sponsorship Of The Unesco Chair In Data Privacy.

the 27 Revised Full Papers Presented Were Carefully Reviewed And Selected From 37 Submissions. The Papers Are Organized In Topical Sections On Tabular Data Protection; Microdata Protection; Online Databases And Remote Access; Privacy-preserving Data Mining And Private Information Retrieval; And Legal Issues.