Statistical Disclosure Control for Microdata

Methods and Applications in R

Nonfiction, Social & Cultural Studies, Social Science, Statistics, Computers, Application Software
Cover of the book Statistical Disclosure Control for Microdata by Matthias Templ, Springer International Publishing
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Author: Matthias Templ ISBN: 9783319502724
Publisher: Springer International Publishing Publication: May 5, 2017
Imprint: Springer Language: English
Author: Matthias Templ
ISBN: 9783319502724
Publisher: Springer International Publishing
Publication: May 5, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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