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.

Available to subscribing member institutions only. Доступно лише організаціям членам підписки.

Online access from local network of NaUOA.

Online access with authorization at https://link.springer.com/

Онлайн-доступ з локальної мережі НаУОА.

Онлайн доступ з авторизацією на https://link.springer.com/