Statistical Disclosure Control for Microdata

Matthias Templ

ISBN 10: 3319843621 / ISBN 13: 9783319843629

Published by Springer International Publishing Jul 2018, 2018

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the data before release. This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences. 308 pp. Englisch.

Seller Inventory # 9783319843629

Bibliographic Details

• Title: Statistical Disclosure Control for Microdata
• Publisher: Springer International Publishing Jul 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 3319843621
• ISBN 13: 9783319843629
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results.

The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the da

ta before release.

This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences.

About the Author

Matthias Templ works as a university lecturer and researcher at the Zurich University of Applied Sciences in Winterthur, Switzerland, and teaches at the Vienna University of Technology in Austria. He is one of the founders of data-analysis OG and holds a PhD in Technical Mathematics and the Venia Legendi (Habilitation) in Statistics. His main research interests include computational statistics and survey methodology. He has been actively involved in many international projects on Statistical Disclosure Control with international organizations (the World Bank, UNIDO, OECD, Eurostat) and has published more than 44 papers in indexed scientific journals in the past few years. He maintains the CRAN Task View on Official Statistics and Survey Methodology and is the author of several R packages for official statistics, such as the R package sdcMicro for statistical disclosure control, the VIM package for the visualization and imputation of missing values, and the package robComposit

ions for the robust analysis of compositional data.