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Statistical Disclosure Control for Microdata: Methods and Applications in R

Editorial SPRINGER VERLAG

Statistical Disclosure Control for Microdata: Methods and Applications in R
-5% dto.    80,00€
76,00€
Ahorra 4,00€
No disponible, consulte disponibilidad
Envío gratis
España peninsular
  • Editorial SPRINGER VERLAG
  • ISBN13 9783319502700
  • ISBN10 3319502700
  • Tipo LIBRO
  • Páginas 287
  • Año de Edición 2017

Statistical Disclosure Control for Microdata: Methods and Applications in R

Editorial SPRINGER VERLAG

-5% dto.    80,00€
76,00€
Ahorra 4,00€
No disponible, consulte disponibilidad
Envío gratis
España peninsular

Detalles del libro

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.

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.