Privacy in Statistical Databases: UNESCO Chair in Data Privacy International Conference, PSD 2008, Istanbul, Turkey, September 24-26, 2008, Proceedings / Edition 1 available in Paperback
- Pub. Date:
- Springer Berlin Heidelberg
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.
Table of ContentsTabular Data Protection.- Using a Mathematical Programming Modeling Language for Optimal CTA.- A Data Quality and Data Confidentiality Assessment of Complementary Cell Suppression.- Pre-processing Optimisation Applied to the Classical Integer Programming Model for Statistical Disclosure Control.- How to Make the ?-ARGUS Modular Method Applicable to Linked Tables.- Bayesian Assessment of Rounding-Based Disclosure Control.- Cell Bounds in Two-Way Contingency Tables Based on Conditional Frequencies.- Invariant Post-tabular Protection of Census Frequency Counts.- Microdata Protection: Methods and Case Studies.- A Practical Approach to Balancing Data Confidentiality and Research Needs: The NHIS Linked Mortality Files.- From t-Closeness to PRAM and Noise Addition Via Information Theory.- Robustification of Microdata Masking Methods and the Comparison with Existing Methods.- A Preliminary Investigation of the Impact of Gaussian Versus t-Copula for Data Perturbation.- Anonymisation of Panel Enterprise Microdata – Survey of a German Project.- Microdata Protection: Disclosure Risk Assessment.- Towards a More Realistic Disclosure Risk Assessment.- Assessing Disclosure Risk for Record Linkage.- Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking.- Parallelizing Record Linkage for Disclosure Risk Assessment.- Extensions of the Re-identification Risk Measures Based on Log-Linear Models.- Use of Auxiliary Information in Risk Estimation.- Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification Disclosure Risks in Partially Synthetic Data.- How Protective Are Synthetic Data?.- On-Line Databases and Remote Access.- Auditing Categorical SUM, MAX and MIN Queries.- Reasoning under Uncertainty in On-Line Auditing.- A Remote Analysis Server - What Does Regression Output Look Like?.- Privacy-Preserving Data Mining and Private Information Retrieval.- Accuracy in Privacy-Preserving Data Mining Using the Paradigm of Cryptographic Elections.- A Privacy-Preserving Framework for Integrating Person-Specific Databases.- Peer-to-Peer Private Information Retrieval.- Legal Issues.- Legal, Political and Methodological Issues in Confidentiality in the European Statistical System.