Statistical Disclosure Control for Microdata

Download or Read online Statistical Disclosure Control for Microdata full in PDF, ePub and kindle. This book written by Matthias Templ and published by Springer which was released on 05 May 2017 with total pages 287. We cannot guarantee that Statistical Disclosure Control for Microdata book is available in the library, click Get Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like.

Statistical Disclosure Control for Microdata
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Publisher : Springer
Release Date :
ISBN : 9783319502724
Pages : 287 pages
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Download or Read Online Statistical Disclosure Control for Microdata in PDF, Epub and Kindle

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.

Statistical Disclosure Control for Microdata

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

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Statistical Disclosure Control

A reference to answer all your statistical confidentialityquestions. This handbook provides technical guidance on statisticaldisclosure control and on how to approach the problem of balancingthe need to provide users with statistical outputs and the need toprotect the confidentiality of respondents. Statistical disclosurecontrol is combined with other tools such as administrative,

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Elements of Statistical Disclosure Control

Statistical disclosure control is the discipline that deals with producing statistical data that are safe enough to be released to external researchers. This book concentrates on the methodology of the area. It deals with both microdata (individual data) and tabular (aggregated) data. The book attempts to develop the theory from

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Statistical Disclosure Control in Practice

The aim of this book is to discuss various aspects associated with disseminating personal or business data collected in censuses or surveys or copied from administrative sources. The problem is to present the data in such a form that they are useful for statistical research and to provide sufficient protection

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Synthetic Datasets for Statistical Disclosure Control

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Advances in Cryptology   CRYPTO 2008

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Manual on Disclosure Control Methods

Download or read online Manual on Disclosure Control Methods written by Barbara Helmpecht,Daniela Schackis, published by Unknown which was released on 1996-01-01. Get Manual on Disclosure Control Methods Books now! Available in PDF, ePub and Kindle.

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Inference Control in Statistical Databases

Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source of data for

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Statistical Confidentiality

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This book constitutes the refereed proceedings of the 16th Australasian Conference on Data Mining, AusDM 2018, held in Bathurst, NSW, Australia, in November 2018. The 27 revised full papers presented together with 3 short papers were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on classification task; transport, environment,

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Medical Data Privacy Handbook

This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information. It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors’ comments. The reuse of EMR data can greatly benefit medical science

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Survey Methods and Practices

This publication shows readers how to design and conduct a census or sample survey. It explains basic survey concepts and provides information on how to create efficient and high quality surveys. It is aimed at those involved in planning, conducting or managing a survey and at students of survey design

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Utilize R to uncover hidden patterns in your Big Data About This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, Explore fast, streaming, and

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Applied Survey Methods

A complete, hands-on guide to the use of statistical methods for obtaining reliable and practical survey research Applied Survey Methods provides a comprehensive outline of the complete survey process, from design to publication. Filling a gap in the current literature, this one-of-a-kind book describes both the theory and practical applications

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Protecting Your Privacy in a Data Driven World

At what point does the sacrifice to our personal information outweigh the public good? If public policymakers had access to our personal and confidential data, they could make more evidence-based, data-informed decisions that could accelerate economic recovery and improve COVID-19 vaccine distribution. However, access to personal data comes at a

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