Description
This comprehensive guide dives into the essential methodologies of statistical disclosure control specifically tailored for microdata. It addresses the critical need for data confidentiality in the era of open data and emphasizes the importance of maintaining privacy while still allowing for robust data analysis. With practical examples and applications utilizing R programming, the author lays out a structured approach for researchers and statisticians alike, making complex statistical techniques accessible.
Readers will appreciate the meticulous explanations of various disclosure control methods, which not only enhance their understanding of protecting sensitive information but also empower them to apply these strategies in real-world datasets. Through a combination of theoretical insights and hands-on applications, the book serves as a crucial resource for anyone looking to navigate the delicate balance between data utility and privacy, ultimately contributing to the responsible use of microdata in research and analysis.
Readers will appreciate the meticulous explanations of various disclosure control methods, which not only enhance their understanding of protecting sensitive information but also empower them to apply these strategies in real-world datasets. Through a combination of theoretical insights and hands-on applications, the book serves as a crucial resource for anyone looking to navigate the delicate balance between data utility and privacy, ultimately contributing to the responsible use of microdata in research and analysis.
Détails du livre
Format
Broché
Pages
306 pages
Langue
Anglais
Publié
Jul 28, 2018
Éditeur
Springer
Édition
Softcover reprint of the original 1st ed. 2017
Éditions
4 éditions
ISBN-10
3319843621
ISBN-13
9783319843629