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Statistical Disclosure Control [Kõva köide]

(Statistics Netherlands), , , , , ,
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 236x158x18 mm, kaal: 522 g
  • Sari: Wiley Series in Survey Methodology
  • Ilmumisaeg: 17-Aug-2012
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119978157
  • ISBN-13: 9781119978152
Teised raamatud teemal:
Statistical Disclosure ControlSuurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 236x158x18 mm, kaal: 522 g
  • Sari: Wiley Series in Survey Methodology
  • Ilmumisaeg: 17-Aug-2012
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119978157
  • ISBN-13: 9781119978152
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119978152.html
Seven European statisticians explore the problem that modern statistics techniques and resources can disclose sensitive information about individuals or entities, and what measure can be taken to prevent unwanted disclosure. They cover a general background of ethics, principles, guidelines, and regulations; microdata; magnitude tabular data; frequency tables; and data access issues. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

A reference to answer all your statistical confidentiality questions.

This handbook provides technical guidance on statistical disclosure control and on how to approach the problem of balancing the need to provide users with statistical outputs and the need to protect the confidentiality of respondents. Statistical disclosure control is combined with other tools such as administrative, legal and IT in order to define a proper data dissemination strategy based on a risk management approach.

The key concepts of statistical disclosure control are presented, along with the methodology and software that can be used to apply various methods of statistical disclosure control. Numerous examples and guidelines are also featured to illustrate the topics covered.

Statistical Disclosure Control:

  • Presents a combination of both theoretical and practical solutions
  • Introduces all the key concepts and definitions involved with statistical disclosure control.
  • Provides a high level overview of how to approach problems associated with confidentiality.
  • Provides a broad-ranging review of the methods available to control disclosure.
  • Explains the subtleties of group disclosure control.
  • Features examples throughout the book along with case studies demonstrating how particular methods are used.
  • Discusses microdata, magnitude and frequency tabular data, and remote access issues.
  • Written by experts within leading National Statistical Institutes.

Official statisticians, academics and market researchers who need to be informed and make decisions on disclosure limitation will benefit from this book.

Preface xi
Acknowledgements xv
1 Introduction
1(9)
1.1 Concepts and definitions
2(5)
1.1.1 Disclosure
2(1)
1.1.2 Statistical disclosure control
3(1)
1.1.3 Tabular data
3(1)
1.1.4 Microdata
3(1)
1.1.5 Risk and utility
4(3)
1.2 An approach to Statistical Disclosure Control
7(2)
1.2.1 Why is confidentiality protection needed?
7(1)
1.2.2 What are the key characteristics and uses of the data?
8(1)
1.2.3 What disclosure risks need to be protected against?
8(1)
1.2.4 Disclosure control methods
8(1)
1.2.5 Implementation
9(1)
1.3 The chapters of the handbook
9(1)
2 Ethics, principles, guidelines and regulations - a general background
10(13)
2.1 Introduction
10(1)
2.2 Ethical codes and the new ISI code
11(5)
2.2.1 ISI Declaration on Professional Ethics
11(1)
2.2.2 New ISI Declaration on Professional Ethics
12(3)
2.2.3 European Statistics Code of Practice
15(1)
2.3 UNECE principles and guidelines
16(3)
2.3.1 UNECE Principles and Guidelines on Confidentiality Aspects of Data Integration
18(1)
2.3.2 Future activities on the UNECE principles and guidelines
19(1)
2.4 Laws
19(4)
2.4.1 Committee on Statistical Confidentiality
20(1)
2.4.2 European Statistical System Committee
20(3)
3 Microdata
23(108)
3.1 Introduction
23(1)
3.2 Microdata concepts
24(12)
3.2.1 Stage 1: Assess need for confidentiality protection
24(3)
3.2.2 Stage 2: Key characteristics and use of microdata
27(3)
3.2.3 Stage 3: Disclosure risk
30(2)
3.2.4 Stage 4: Disclosure control methods
32(2)
3.2.5 Stage 5: Implementation
34(2)
3.3 Definitions of disclosure
36(2)
3.3.1 Definitions of disclosure scenarios
37(1)
3.4 Definitions of disclosure risk
38(5)
3.4.1 Disclosure risk for categorical quasi-identifiers
39(1)
3.4.2 Notation and assumptions
40(1)
3.4.3 Disclosure risk for continuous quasi-identifiers
41(2)
3.5 Estimating re-identification risk
43(8)
3.5.1 Individual risk based on the sample: Threshold rule
44(1)
3.5.2 Estimating individual risk using sampling weights
44(3)
3.5.3 Estimating individual risk by Poisson model
47(1)
3.5.4 Further models that borrow information from other sources
48(1)
3.5.5 Estimating per record risk via heuristics
49(1)
3.5.6 Assessing risk via record linkage
50(1)
3.6 Non-perturbative microdata masking
51(2)
3.6.1 Sampling
51(1)
3.6.2 Global recoding
52(1)
3.6.3 Top and bottom coding
53(1)
3.6.4 Local suppression
53(1)
3.7 Perturbative microdata masking
53(25)
3.7.1 Additive noise masking
54(3)
3.7.2 Multiplicative noise masking
57(3)
3.7.3 Microaggregation
60(12)
3.7.4 Data swapping and rank swapping
72(1)
3.7.5 Data shuffling
73(1)
3.7.6 Rounding
73(1)
3.7.7 Re-sampling
74(1)
3.7.8 PRAM
74(4)
3.7.9 MASSC
78(1)
3.8 Synthetic and hybrid data
78(22)
3.8.1 Fully synthetic data
79(5)
3.8.2 Partially synthetic data
84(2)
3.8.3 Hybrid data
86(12)
3.8.4 Pros and cons of synthetic and hybrid data
98(2)
3.9 Information loss in microdata
100(10)
3.9.1 Information loss measures for continuous data
101(7)
3.9.2 Information loss measures for categorical data
108(2)
3.10 Release of multiple files from the same microdata set
110(1)
3.11 Software
111(5)
3.11.1 μ-ARGUS
111(2)
3.11.2 sdcMicro
113(2)
3.11.3 IVEware
115(1)
3.12 Case studies
116(15)
3.12.1 Microdata files at Statistics Netherlands
116(2)
3.12.2 The European Labour Force Survey microdata for research purposes
118(3)
3.12.3 The European Structure of Earnings Survey microdata for research purposes
121(7)
3.12.4 NHIS-linked mortality data public use file, USA
128(2)
3.12.5 Other real case instances
130(1)
4 Magnitude tabular data
131(52)
4.1 Introduction
131(7)
4.1.1 Magnitude tabular data: Basic terminology
131(1)
4.1.2 Complex tabular data structures: Hierarchical and linked tables
132(2)
4.1.3 Risk concepts
134(3)
4.1.4 Protection concepts
137(1)
4.1.5 Information loss concepts
137(1)
4.1.6 Implementation: Software, guidelines and case study
138(1)
4.2 Disclosure risk assessment I: Primary sensitive cells
138(14)
4.2.1 Intruder scenarios
138(2)
4.2.2 Sensitivity rules
140(12)
4.3 Disclosure risk assessment II: Secondary risk assessment
152(5)
4.3.1 Feasibility interval
152(2)
4.3.2 Protection level
154(1)
4.3.3 Singleton and multi cell disclosure
155(1)
4.3.4 Risk models for hierarchical and linked tables
155(2)
4.4 Non-perturbative protection methods
157(6)
4.4.1 Global recoding
157(1)
4.4.2 The concept of cell suppression
157(1)
4.4.3 Algorithms for secondary cell suppression
158(3)
4.4.4 Secondary cell suppression in hierarchical and linked tables
161(2)
4.5 Perturbative protection methods
163(3)
4.5.1 A pre-tabular method: Multiplicative noise
165(1)
4.5.2 A post-tabular method: Controlled tabular adjustment
165(1)
4.6 Information loss measures for tabular data
166(2)
4.6.1 Cell costs for cell suppression
166(1)
4.6.2 Cell costs for CTA
167(1)
4.6.3 Information loss measures to evaluate the outcome of table protection
167(1)
4.7 Software for tabular data protection
168(5)
4.7.1 Empirical comparison of cell suppression algorithms
169(4)
4.8 Guidelines: Setting up an efficient table model systematically
173(5)
4.8.1 Defining spanning variables
174(1)
4.8.2 Response variables and mapping rules
175(3)
4.9 Case studies
178(5)
4.9.1 Response variables and mapping rules of the case study
178(1)
4.9.2 Spanning variables of the case study
179(1)
4.9.3 Analysing the tables of the case study
179(2)
4.9.4 Software issues of the case study
181(2)
5 Frequency tables
183(25)
5.1 Introduction
183(1)
5.2 Disclosure risks
184(7)
5.2.1 Individual attribute disclosure
185(1)
5.2.2 Group attribute disclosure
186(1)
5.2.3 Disclosure by differencing
187(3)
5.2.4 Perception of disclosure risk
190(1)
5.3 Methods
191(2)
5.3.1 Pre-tabular
191(1)
5.3.2 Table re-design
192(1)
5.3.3 Post-tabular
193(1)
5.4 Post-tabular methods
193(6)
5.4.1 Cell suppression
193(1)
5.4.2 ABS cell perturbation
193(1)
5.4.3 Rounding
194(5)
5.5 Information loss
199(2)
5.6 Software
201(3)
5.6.1 Introduction
201(1)
5.6.2 Optimal, first feasible and RAPID solutions
202(1)
5.6.3 Protection provided by controlled rounding
203(1)
5.7 Case studies
204(4)
5.7.1 UK Census
204(1)
5.7.2 Australian and New Zealand Censuses
205(3)
6 Data access issues
208(35)
6.1 Introduction
208(1)
6.2 Research data centres
209(1)
6.3 Remote execution
209(1)
6.4 Remote access
210(1)
6.5 Licensing
211(1)
6.6 Guidelines on output checking
211(25)
6.6.1 Introduction
211(1)
6.6.2 General approach
212(3)
6.6.3 Rules for output checking
215(9)
6.6.4 Organisational/procedural aspects of output checking
224(9)
6.6.5 Researcher training
233(3)
6.7 Additional issues concerning data access
236(1)
6.7.1 Examples of disclaimers
236(1)
6.7.2 Output description
236(1)
6.8 Case studies
237(6)
6.8.1 The US Census Bureau Microdata Analysis System
237(2)
6.8.2 Remote access at Statistics Netherlands
239(4)
Glossary 243(18)
References 261(18)
Author index 279(3)
Subject index 282
Anco Hundepool, Statistics Netherlands, The Netherlands.

Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain.

Luisa Franconi, Head of Unit on Statistical Disclosure Control Methods, ISTAT, Italy.

Sarah Giessing, Federal Statistical Office of Germany, Germany.

Keith Spicer, Office for National Statistics, Portsmouth, UK.

Eric Schulte Nordholt, Senior researcher and project leader at Statistics, The Netherlands.

Peter-Paul De Wolf, Methodologist at National Institute of Statistics, The Netherlands.