Statistical disclosure control for microdata: methods and applications in R
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| Format: | Electronic eBook |
| Language: | English |
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Cham
Springer
[2017]
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| Subjects: | |
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| Physical Description: | 1 Online-Ressource (XIX, 287 Seiten, 37 illus., 27 illus. in color) |
| ISBN: | 9783319502724 |
| DOI: | 10.1007/978-3-319-50272-4 |
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Record in the Search Index
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|---|---|
| _version_ | 1824554602955014145 |
| adam_text | Titel: Statistical disclosure control for microdata
Autor: Templ, Matthias
Jahr: 2017
Contents
1 Software................................................. 1
1.1 Prerequisites......................................... 1
1.1.1 Installation and Updates.......................... 2
1.1.2 Install sdcMicro and Its Browser-Based
Point-and-Click App............................. 3
1.1.3 Updating the SDC Tools.......................... 3
1.1.4 Help......................................... 3
1.1.5 The R Workspace and the Working Directory......... 5
1.1.6 Data Types.................................... 5
1.1.7 Generic Functions, Methods and Classes............. 11
1.2 Brief Overview on SDC Software Tools.................... 14
1.3 Differences Between SDC Tools.......................... 15
1.4 Working with sdcMicro................................ 17
1.4.1 General Information About sdcMicro................ 18
1.4.2 S4 Class Structure of the sdcMicro Package........... 18
1.4.3 Utility Functions................................ 23
1.4.4 Reporting Facilities.............................. 25
1.5 The Point-and-Click App sdcApp......................... 26
1.6 The simPop package................................... 31
References................................................ 33
2 Basic Concepts............................................ 35
2.1 Types of Variables.................................... 35
2.1.1 Non-confidential Variables........................ 35
2.1.2 Identifying Variables............................. 36
2.1.3 Sensitive Variables.............................. 36
2.1.4 Linked Variables................................ 37
2.1.5 Sampling Weights............................... 37
2.1.6 Hierarchies, Clusters and Strata..................... 38
2.1.7 Categorical Versus Continuous Variables............. 38
xiv Contents
2.2 Types of Disclosure................................... 38
2.2.1 Identity Disclosure.............................. 39
2.2.2 Attribute Disclosure............................. 39
2.2.3 Inferential Disclosure............................ 40
2.3 Disclosure Risk Versus Information Loss and Data Utility...... 42
2.4 Release Types........................................ 45
2.4.1 Public Use Files (PUF)........................... 45
2.4.2 Scientific Use Files (SUF)......................... 46
2.4.3 Controlled Research Data Center................... 46
2.4.4 Remote Execution............................... 47
2.4.5 Remote Access................................. 47
References................................................ 48
3 Disclosure Risk........................................... 49
3.1 Introduction......................................... 49
3.2 Frequency Counts..................................... 50
3.2.1 The Number of Cells of Equal Size................. 51
3.2.2 Frequency Counts with Missing Values.............. 53
3.2.3 Sample Frequencies in sdcMicro.................... 54
3.3 Principles of ^-anonymity and /-diversity................... 58
3.3.1 Simplified Estimation of Population
Frequency Counts............................... 60
3.4 Special Uniques Detection Algorithm (SUDA)............... 67
3.4.1 Minimal Sample Uniqueness....................... 68
3.4.2 SUDA Scores.................................. 68
3.4.3 SUDA DIS Scores.............................. 69
3.4.4 SUDA in sdcMicro.............................. 69
3.5 The Individual Risk Approach........................... 72
3.5.1 The Benedetti-Franconi Model for Risk Estimation..... 73
3.6 Disclosure Risks for Hierarchical Data..................... 75
3.7 Measuring Global Risks................................ 77
3.7.1 Measuring the Global Risk
Using Log-Linear Models:........................ 79
3.7.2 Standard Log-Linear Model....................... 79
3.7.3 Clogg and Eliason Method........................ 79
3.7.4 Pseudo Maximum Likelihood Method............... 80
3.7.5 Weighted Log-Linear Model....................... 80
3.8 Application of the Log-Linear Models..................... 80
3.9 Global Risk Measures.................................. 85
3.10 Quality of the Risk Measures Under Different Sampling
Designs............................................. 90
3.11 Disclosure Risk for Continuous Variables.................. 91
Contents xv
3.12 Special Treatment of Outliers When Calculating Disclosure
Risks............................................... 93
References................................................ 96
4 Methods for Data Perturbation.............................. 99
4.1 Kind of Methods..................................... 99
4.2 Methods for Categorical Key Variables.................... 100
4.2.1 Recoding...................................... 100
4.2.2 Local Suppression............................... 103
4.2.3 Post-randomization Method (PRAM)................ 116
4.3 Methods for Continuous Key Variables.................... 119
4.3.1 Microaggregation............................... 119
4.3.2 Noise Addition................................. 125
4.3.3 Shuffling...................................... 130
References................................................ 132
5 Data Utility and Information Loss............................ 133
5.1 Element-Wise Comparisons............................. 133
5.1.1 Comparing Missing Values........................ 133
5.1.2 Comparing Aggregated Information................. 134
5.2 Element-Wise Measures for Continuous Variables............ 139
5.2.1 Element-Wise Comparisons of Mixed
Scaled Variables................................ 143
5.3 Entropy............................................. 144
5.4 Propensity Score Methods.............................. 145
5.5 Quality Indicators..................................... 148
5.5.1 General Procedure............................... 148
5.5.2 Differences in Point Estimates...................... 149
5.5.3 Differences in Variances and MSE.................. 150
5.5.4 Overlap in Confidence Intervals.................... 151
5.5.5 Differences in Model Estimates..................... 153
References................................................ 155
6 Synthetic Data............................................ 157
6.1 Introduction......................................... 157
6.2 Model-Based Generation of Synthetic Data................. 159
6.2.1 Setup of the Structure............................ 161
6.2.2 Simulation of Categorical Variables................. 162
6.2.3 Simulation of Continuous Variables................. 164
6.2.4 Splitting Continuous Variables into Components....... 168
6.3 Disclosure Risk of Synthetic Data........................ 169
6.3.1 Confidentiality of Synthetic Population Data........... 171
6.3.2 Disclosure Scenarios for Synthetic Population Data..... 172
6.4 Data Utility of Synthetic Data........................... 176
References................................................ 178
xvi Contents
7 Practical Guidelines........................................ 181
7.1 The Workflow....................................... 181
7.2 How to Determine the Key Variables...................... 182
7.3 The Level of Disclosure Risk Versus Information Loss........ 183
7.4 Which SDC Methods Should Be Used..................... 183
References................................................ 186
8 Case Studies.............................................. 187
8.1 Practical Issues....................................... 187
8.2 Anonymization of the FIES Data......................... 188
8.2.1 FDES Data Description........................... 188
8.2.2 Pre-processing Steps............................. 189
8.2.3 Frequency Counts and Disclosure Risk............... 190
8.2.4 Recoding...................................... 191
8.2.5 Local Suppression............................... 192
8.2.6 Perturbing the Continuous Key Variables............. 193
8.2.7 PRAM........................................ 194
8.2.8 Remark....................................... 195
8.3 Application to the Structural Earnings Statistics
(SES) Survey........................................ 195
8.3.1 General Information About SES.................... 195
8.3.2 Details on Some Variables........................ 196
8.3.3 Applications and Statistics Based on SES............. 198
8.3.4 The Synthetic SES Data.......................... 199
8.3.5 Key Variables for Re-identification.................. 199
8.3.6 Pre-processing Steps............................. 200
8.3.7 Risk Estimation................................. 201
8.3.8 Perturbing the Continuous Scaled Variables........... 203
8.3.9 Measuring the Data Utility........................ 204
8.4 I2D2............................................... 213
8.4.1 About I2D2 Data............................... 213
8.4.2 Disclosure Scenario/Key Variables.................. 213
8.4.3 Anonymization of One Example Country............. 214
8.4.4 Results for All Other Countries..................... 219
8.4.5 Data Utility.................................... 221
8.5 Anonymization of P4 Data.............................. 222
8.5.1 Key Variables.................................. 222
8.5.2 Key Variables on Individual Level.................. 223
8.5.3 sdcMicro Code for One Example Country............ 223
8.5.4 Results for All Other Countries..................... 226
8.6 Anonymization of the SHIP Data......................... 227
8.6.1 Key Variables.................................. 228
8.6.2 sdcMicro Code for One Example Country............ 229
8.6.3 Results for All Other Countries..................... 233
Contents xvii
8.7 A Synthetic Socio-economic Population and Sample.......... 236
8.7.1 Data Preprocessing.............................. 237
8.7.2 Simulation of the Population....................... 243
8.7.3 Optionally: Draw a Sample from the Population........ 250
8.7.4 Exploration of the Final Synthetic Population
and Sample.................................... 251
References................................................ 258
Software Versions Used in the Book............................. 261
Solutions................................................... 263
Index...................................................... 285
|
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| indexdate | 2025-02-20T06:54:23Z |
| institution | BVB |
| isbn | 9783319502724 |
| language | English |
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| publishDate | 2017 |
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| publisher | Springer |
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| spellingShingle | Templ, Matthias Statistical disclosure control for microdata methods and applications in R Statistics Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistics and Computing/Statistics Programs Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Life Sciences, Medicine, Health Sciences Statistik |
| title | Statistical disclosure control for microdata methods and applications in R |
| title_auth | Statistical disclosure control for microdata methods and applications in R |
| title_exact_search | Statistical disclosure control for microdata methods and applications in R |
| title_full | Statistical disclosure control for microdata methods and applications in R Matthias Templ |
| title_fullStr | Statistical disclosure control for microdata methods and applications in R Matthias Templ |
| title_full_unstemmed | Statistical disclosure control for microdata methods and applications in R Matthias Templ |
| title_short | Statistical disclosure control for microdata |
| title_sort | statistical disclosure control for microdata methods and applications in r |
| title_sub | methods and applications in R |
| topic | Statistics Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistics and Computing/Statistics Programs Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Life Sciences, Medicine, Health Sciences Statistik |
| topic_facet | Statistics Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistics and Computing/Statistics Programs Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Life Sciences, Medicine, Health Sciences Statistik |
| url | https://doi.org/10.1007/978-3-319-50272-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029738159&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT templmatthias statisticaldisclosurecontrolformicrodatamethodsandapplicationsinr |