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＜電子ブック＞
Statistical Disclosure Control for Microdata : Methods and Applications in R / by Matthias Templ

版	1st ed. 2017.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2017
本文言語	英語
大きさ	XIX, 287 p. 37 illus., 27 illus. in color : online resource
著者標目	*Templ, Matthias author SpringerLink (Online service)
件　名	LCSH:Social sciences -- Statistical methods  全ての件名で検索 LCSH:Mathematical statistics -- Data processing  全ての件名で検索 LCSH:Statistics  LCSH:Biometry FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistics and Computing FREE:Statistical Theory and Methods FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Biostatistics
一般注記	Preface -- Software -- Basic Concepts -- Disclosure Risk -- Methods for Data Perturbation -- Data Utility and Information Loss -- Synthetic Data -- Practical Guidelines -- Case Studies -- Solutions -- Index 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 dat a 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 HTTP:URL=https://doi.org/10.1007/978-3-319-50272-4

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