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Privacy in Statistical Databases

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Cover of 'Privacy in Statistical Databases'

Table of Contents

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    Book Overview
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    Chapter 1 Using a Mathematical Programming Modeling Language for Optimal CTA
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    Chapter 2 A Data Quality and Data Confidentiality Assessment of Complementary Cell Suppression
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    Chapter 3 Pre-processing Optimisation Applied to the Classical Integer Programming Model for Statistical Disclosure Control
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    Chapter 4 How to Make the τ -ARGUS Modular Method Applicable to Linked Tables
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    Chapter 5 Bayesian Assessment of Rounding-Based Disclosure Control
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    Chapter 6 Cell Bounds in Two-Way Contingency Tables Based on Conditional Frequencies
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    Chapter 7 Invariant Post-tabular Protection of Census Frequency Counts
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    Chapter 8 A Practical Approach to Balancing Data Confidentiality and Research Needs: The NHIS Linked Mortality Files
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    Chapter 9 From t -Closeness to PRAM and Noise Addition Via Information Theory
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    Chapter 10 Robustification of Microdata Masking Methods and the Comparison with Existing Methods
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    Chapter 11 A Preliminary Investigation of the Impact of Gaussian Versus t-Copula for Data Perturbation
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    Chapter 12 Anonymisation of Panel Enterprise Microdata – Survey of a German Project
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    Chapter 13 Towards a More Realistic Disclosure Risk Assessment
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    Chapter 14 Assessing Disclosure Risk for Record Linkage
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    Chapter 15 Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking
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    Chapter 16 Parallelizing Record Linkage for Disclosure Risk Assessment
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    Chapter 17 Extensions of the Re-identification Risk Measures Based on Log-Linear Models
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    Chapter 18 Use of Auxiliary Information in Risk Estimation
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    Chapter 19 Accounting for Intruder Uncertainty Due to Sampling When Estimating Identification Disclosure Risks in Partially Synthetic Data
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    Chapter 20 How Protective Are Synthetic Data?
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    Chapter 21 Auditing Categorical SUM, MAX and MIN Queries
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    Chapter 22 Reasoning under Uncertainty in On-Line Auditing
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    Chapter 23 A Remote Analysis Server - What Does Regression Output Look Like?
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    Chapter 24 Accuracy in Privacy-Preserving Data Mining Using the Paradigm of Cryptographic Elections
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    Chapter 25 A Privacy-Preserving Framework for Integrating Person-Specific Databases
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    Chapter 26 Peer-to-Peer Private Information Retrieval
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    Chapter 27 Legal, Political and Methodological Issues in Confidentiality in the European Statistical System
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Title
Privacy in Statistical Databases
Published by
Springer Science & Business Media, September 2008
DOI 10.1007/978-3-540-87471-3
ISBNs
978-3-54-087470-6, 978-3-54-087471-3
Editors

Domingo-Ferrer, Josep, Saygın, Yücel

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 20%
France 1 20%
Unknown 3 60%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Researcher 2 40%
Student > Bachelor 1 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 20%
Computer Science 1 20%
Psychology 1 20%
Medicine and Dentistry 1 20%
Engineering 1 20%
Other 0 0%