Statistical Disclosure Control for Microdata - Practical R

In this review of Statistical Disclosure Control for Microdata, the bottom line is clear: researchers and data custodians who need a rigorous, reproducible path to anonymizing microdata will find one comprehensive resource here. The book combines theory, applied methods and R code so readers can move from concepts like disclosure risk to practical implementations with sdcMicro and simPop. It is most valuable to applied statisticians, survey methodologists and privacy practitioners who want worked examples and case studies they can run and adapt.

Key Features

• Theory and methods: Presents the foundations of statistical disclosure control so readers understand why particular anonymization methods are used and what trade-offs they imply.
• Practical R code: Includes runnable examples and the underlying R code that allow readers to reproduce analyses using sdcMicro and simPop.
• Disclosure risk and utility: Explains how to measure disclosure risk alongside information loss, helping users choose methods that balance privacy and data usefulness.
• Data perturbation techniques: Covers perturbation and related approaches so practitioners can apply techniques to microdata from surveys or registers.
• Case studies with solutions: Provides exercises with solutions and real-world case studies that illustrate end-to-end application of methods.

Who It's For

This book is ideal for applied statisticians, data managers and academic researchers working with person-level or enterprise microdata who need a thorough, hands-on treatment of anonymization. The presence of R code and case studies makes it suited for users who want to reproduce results or adapt workflows to their datasets.

Readers who seek an introductory primer with minimal mathematics or no programming will find parts of the text dense; likewise, advanced theorists looking for new cutting-edge privacy theorems may prefer more specialized research papers. This book sits squarely between method and practice.

Pros & Cons

Pros

• Combines theoretical background with practical R implementations for direct reproducibility.
• Includes detailed case studies and exercises with solutions to reinforce learning.
• Explains both disclosure risk metrics and information loss so users can evaluate trade-offs.

Cons

• Material assumes familiarity with R, so complete beginners will need prior programming experience.

Specifications

Title	Statistical Disclosure Control for Microdata
Author	Matthias Templ
Focus	Methods, applications and software implementation for microdata anonymization
Software covered	sdcMicro and simPop in R
Includes	Examples, exercises with solutions and case studies with R code
Primary topics	Data perturbation, disclosure risk, data utility and synthetic data

Our Verdict

Statistical Disclosure Control for Microdata is a practical, well-structured resource for professionals who must anonymize microdata and reproduce analyses with R. Its combination of theory, examples and code delivers strong value for applied statisticians and data custodians, though newcomers to R will face a learning curve before leveraging the full benefit.

Frequently Asked Questions

Does the book include code I can run?
Yes. The text provides the underlying R code and examples using the sdcMicro and simPop packages so readers can reproduce results.

What topics does it cover on privacy?
The book covers disclosure risk assessment, information loss measures, data perturbation methods and approaches to simulating synthetic data.

Who needs prior knowledge to use this book?
Familiarity with R and basic statistical concepts is recommended to make full use of the examples and exercises.