Statistical Disclosure Control for Microdata: Theory

This guide provides and introduction to the theory of Statistical Disclosure Control (SDC) for microdata. It includes an overview of the most commonly applied methods in SDC, a step-by-step overview of the complete SDC process and many examples from practice in National Statistics Offices (NSOs).

For guidance on the technical implementation of the theory mentioned in the guide, please refer to our guides:

1. Statistical Disclosure Control for Microdata: A Practice Guide for guidance on the application of methods and on using sdcMicro from the command-line
2. sdcApp manual for guidance on the application of methods and on using the GUI sdcApp available for sdcMicro

Authors of this guide: Thijs Benschop and Matthew Welch, The World Bank

Acknowledgments: The authors thank Olivier Dupriez and Cathrine Machingauta (The World Bank) for their technical comments and inputs throughout the process.

The production of this guide was made possible through a World Bank Knowledge for Change II Grant: KCP II - A microdata dissemination challenge: Balancing data protection and data utility. Grant number: TF 015043, Project Number P094376. As well as from United Kingdom - DFID funding to the World Bank Multi-Donor Trust Fund - International Household Survey and Accelerated Data Program – TF071804/TF011722/TF0A7461.

Table of Content

• Introduction
  • Building a knowledge base
  • Using this guide
  • Statistical software tools for technical implementation
  • Outline of this guide
• Glossary and list of acronyms
  • List of Acronyms
  • Glossary
• Statistical Disclosure Control (SDC): An Introduction
  • Need for SDC
  • The risk-utility trade-off in the SDC process
• Release Types
  • Conditions for PUFs
  • Conditions for SUFs
  • Conditions for microdata available in a controlled research data center
• Measuring Risk
  • Types of disclosure
  • Classification of variables
  • Disclosure scenarios
  • Levels of risk
  • Individual risk
  • Special Uniques Detection Algorithm (SUDA)
  • Risk measures for continuous variables
  • Global risk
  • Household risk
• Anonymization Methods
  • Classification of SDC methods
  • Non-perturbative methods
  • Perturbative methods
  • Anonymization of geospatial variables
  • Anonymization of the quasi-identifier household size
  • Special case: census data
• Measuring Utility and Information Loss
  • General utility measures for continuous and categorical variables
  • Utility measures based on the end user’s needs
  • Regression
  • Assessing data utility with the help of data visualizations
  • Choice of utility measure
• The SDC Process
  • Step 1: Need for confidentiality protection
  • Step 2: Data preparation and exploring data characteristics
  • Step 3: Type of release
  • Step 4: Intruder scenarios and choice of key variables
  • Step 5: Data key uses and selection of utility measures
  • Step 6: Assessing disclosure risk
  • Step 7: Assessing utility measures
  • Step 8: Choice and application of SDC methods
  • Step 9: Re-measure risk
  • Step 10: Re-measure utility
  • Step 11: Audit and Reporting
  • Step 12: Data release
• Case Studies (Illustrating the SDC Process)
  • Case study 1- SUF
  • Case study 2 - PUF
• Appendices
  • Appendix A: Overview of Case Study Variables
  • Appendix B: Example of Blanket Agreement for SUF
  • Appendix C: Internal and External Reports for Case Studies
  • Case study 1 - Internal report
  • Case study 1 - External report
  • Case study 2 - Internal report
  • Case study 2- External report
  • Appendix D: Execution Times for Multiple Scenarios Tested using Selected Sample Data
• Acknowledgements