UID:
almahu_9947362961802882
Format:
XV, 261 p.
,
online resource.
ISBN:
9781461301219
Series Statement:
Lecture Notes in Statistics, 155
Content:
Statistical disclosure control is the discipline that deals with producing statistical data that are safe enough to be released to external researchers. This book concentrates on the methodology of the area. It deals with both microdata (individual data) and tabular (aggregated) data. The book attempts to develop the theory from what can be called the paradigm of statistical confidentiality: to modify unsafe data in such a way that safe (enough) data emerge, with minimum information loss. This book discusses what safe data, are, how information loss can be measured, and how to modify the data in a (near) optimal way. Once it has been decided how to measure safety and information loss, the production of safe data from unsafe data is often a matter of solving an optimization problem. Several such problems are discussed in the book, and most of them turn out to be hard problems that can be solved only approximately. The authors present new results that have not been published before. The book is not a description of an area that is closed, but, on the contrary, one that still has many spots awaiting to be more fully explored. Some of these are indicated in the book. The book will be useful for official, social and medical statisticians and others who are involved in releasing personal or business data for statistical use. Operations researchers may be interested in the optimization problems involved, particularly for the challenges they present. Leon Willenborg has worked at the Department of Statistical Methods at Statistics Netherlands since 1983, first as a researcher and since 1989 as a senior researcher. Since 1989 his main field of research and consultancy has been statistical disclosure control. From 1996-1998 he was the project coordinator of the EU co-funded SDC project.
Note:
1 Overview of the Area -- 1.1 Introduction -- 1.2 Types of Variables -- 1.3 Types of Microdata -- 1.4 Types of Tabular Data -- 1.5 Introduction to SDC for Microdata and Tables -- 1.6 Intruders and Disclosure Scenarios -- 1.7 Information Loss -- 1.8 Disclosure Protection Techniques for Microdata -- 1.9 Disclosure Protection Techniques for Tables -- 2 Disclosure Risks for Microdata -- 2.1 Introduction -- 2.2 Microdata -- 2.3 Disclosure Scenario -- 2.4 Predictive Disclosure -- 2.5 Re-identification Risk -- 2.6 Risk Per Record and Overall Risk -- 2.7 Population Uniqueness and Unsafe Combinations -- 2.8 Modeling Risks with Discrete Key Variables -- 2.9 Disclosure Scenarios in Practice -- 2.10 Combinations to Check -- 3 Data Analytic Impact of SDC Techniques on Microdata -- 3.1 Introduction -- 3.2 The Variance Impact of SDC Procedures -- 3.3 The Bias Impact of SDC Procedures -- 3.4 Impact of SDC Procedures on Methods of Estimation -- 3.5 Information Loss Measures Based on Entropy -- 3.6 Alternative Information Loss Measures -- 3.7 MSP for Microdata -- 4 Application of Non-Perturbative SDC Techniques for Microdata -- 4.1 Introduction -- 4.2 Local Suppression -- 4.3 Global Recoding -- 5 Application of Perturbative SDC Techniques for Microdata -- 5.1 Introduction -- 5.2 Overview -- 5.3 Adding Noise -- 5.4 Rounding -- 5.5 Derivation of PRAM Matrices -- 5.6 Data Swapping -- 5.7 Adjustment Weights -- 6 Disclosure Risk for Tabular Data -- 6.1 Introduction -- 6.2 Disclosur e Risk for Tables of Magnitude Tables -- 6.3 Disclosure Risk for Frequency Count Tables -- 6.4 Linked Tables -- 6.5 Protection Intervals for Sensitive Cells -- 6.6 Sensitivity Rules for General Tables -- 7.2 Information Loss Based on Cell Weights -- 7.3 MSP for Tables -- 7.4 Entropy Considerations -- 8 Application of Non-Perturbative Techniques for Tabular Data -- 8.1 Introduction -- 8.2 Table Redesign -- 8.3 Cell Suppression -- 8.4 Some Additional Cell Suppression Terminology -- 8.5 Hypercube Method -- 8.6 Secondary Suppression as an LP-Problem -- 8.7 Secondary Suppression as a MIP -- 8.8 Cell Suppression in Linked Tables -- 8.9 Cell Suppression in General Two-Dimensional Tables -- 8.10 Cell Suppression in General Three-Dimensional Tables -- 8.11 Comments on Cell Suppression -- 9 Application of Perturbative Techniques for Tabular Data -- 9.1 Introduction -- 9.2 Adding Noise -- 9.3 Unrestricted Rounding -- 9.4 Controlled Rounding -- 9.5 Controlled Rounding by Means of Simulated Annealing -- 9.6 Controlled Rounding as a MIP -- 9.7 Linked Tables -- References.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9780387951218
Language:
English
Subjects:
Mathematics
DOI:
10.1007/978-1-4613-0121-9
URL:
http://dx.doi.org/10.1007/978-1-4613-0121-9
URL:
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