Statistical disclosure control covers a range of methods to protect individuals, households, businesses and their attributes (characteristics) from identification in published tables (and microdata). suppression, rounding, perturbation, additive noise ; Jobholder can evaluate the utility of the data following the application of statistical disclosure control methods PDF | On Dec 31, 2005, M.J. Elliot published Statistical Disclosure Control | Find, read and cite all the research you need on ResearchGate We refer to Statistical Disclosure Control for Microdata: A Theory Guide. Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata.The purpose of SDC is to protect the confidentiality . •(Increasing) need for SDC •General SDC issues •R-U map This process, called statistical disclosure control, can be carried out in various ways. Statistical agencies have statutory and ethical obligations to protect the confidentiality of the data they collect. sdcTarget: Statistical Disclosure Control Substitution Matrix Calculator Classes and methods to calculate and evaluate target matrices for statistical disclosure control. Traditionally SDC methods were associated with protecting tables. Methods for SDC usually restrict the amount of, or reduce, the detail of the data released. Version: SDCMicro is free, R-based open-source package for the generation of protected microdata for researchers and public use. SDC's subsequent emergence as a specialised academic field was an outcome of three interrelated socio-technical . Effective dis-closure control techniques reduce to an acceptable level the likelihood that either a respondent may be identified through its responses or that data collected from an The most traditional are tables of descriptive estimates, . This technique has primarily been used by National Statistical Offices (NSOs) and other statistical . 3. A microdata file is a data matrix where each row, called a record, corresponds to one respondent and where the columns correspond to the variables. There are various kinds of statistical outputs from surveys. Statistical Disclosure Control (2012) by A. Hundepool, J. Domingo-Ferrer, L. Franconi, S. Giessing, E. Schulte Nordholt, K. Spicer and P.P. SDC usually refers to 'output SDC'; ensuring that, for example, a published table or . This Handbook is intended for use by both analysts and staff responsible for carrying out statistical disclosure control checks. Revenue's obligations to safeguard data are established by a range of legislative and administrative provisions designed to protect the rights and interests of citizens and businesses. Statistical disclosure control is combined with other tools such as administrative, legal and IT in order to define a proper data dissemination strategy based on a risk management approach. A number of Safe Settings operate throughout the UK. The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. 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, Methods and Applications in R, Templ, Matthias, Buch Supposingwe have some data about a bunch of companies operatingin differentsectors. The Netherlands. General provisions include: Microaggregation is an efficient Statistical Disclosure Control perturbative technique for microdata protection i.e. It includes an overview of the most commonly applied methods in SDC, a step-by-step overview of the complete SDC process and many examples from practice in National Statistics Offices . The problem is to present the data in such a form that they are useful for statistical research and to provide sufficient protection for the individuals or businesses to whom the data refer. Last update: 26 August 2021. Statistical disclosure control. Guidance for birth and death statistics Guidance on the circumstances in which disclosure control for birth and death statistics is required. The key concepts of statistical disclosure control are presented, along with the methodology and software that can be used to apply various methods of statistical disclosure control. 1.2 An approach to Statistical Disclosure Control 7 1.2.1 Why is confidentiality protection needed? 7 1.2.2 What are the key characteristics and uses of the data? of disclosure risk (i.e. The risk of disclosing an individual's identity in this analysis has been assessed and statistical disclosure control has been applied to the data accordingly. Output statistical disclosure control (SDC) techniques have been developed for these environments, but they are designed to deal with statistical output such as tabulations, coefficient estimates, or graphs. Statistical disclosure control (SDC) was not created in a single seminal paper nor following the invention of a new mathematical technique, rather it developed slowly in response to the practical challenges faced by data practitioners based at national statistical institutes (NSIs). Disclosure control refers to the measures taken to protect data in accordance with confidentiality requirements. This handbook provides technical guidance on statistical disclosure control and on how to approach the problem of balancing the need to provide users with statistical outputs and the need to protect the confidentiality of respondents. Methods relating to the first kind of assurance, for example, computer security and staff protocols for the . To handle the microdata shared on HDX, we use an open-source software package for Statistical Disclosure Control (SDC) called sdcMicro. Statistical Disclosure Control - Ebook written by Josep Domingo-Ferrer, Luisa Franconi, Sarah Giessing, Eric Schulte Nordholt, Keith Spicer, Peter-Paul de Wolf, Anco Hundepool. It contains guidance about how to assess statistical results produced from confidential sources of data, before they are released from the safe setting and into the wider world. A reference to answer all your statistical confidentiality questions. Statistical disclosure control, also known as statistical disclosure limitation or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata. Read reviews from world's largest community for readers. Having successfully completed this module you will be able to: Choose appropriate methods of statistical disclosure control for particular types of data and situations, and apply them by hand in simple situations. This is a relatively unexplored field: only a handful of papers have been produced over Statistical disclosure control. We call this process statistical disclosure. It is also a very interesting problem for theoretical statisticians from the viewpoint of statistical inference. Statistical Disclosure Control. The major part of this book is concerned with how to define the disclosure problem and how to deal with it in practical circumstances. Statistical Disclosure Control von Anco Hundepool, Josep Domingo-Ferrer, Luisa Franconi, Sarah Giessing, Eric Schulte Nordholt, Keith Spicer, Peter-Paul de Wolf - Jetzt bei yourbook.shop kaufen und mit jedem Kauf Deine Lieblings-Buchhandlung unterstützen! NHS National Services Scotland - Statistical Disclosure Control Protocol - Version 3.0 4 2 Background Reliable health statistics are a pre-requisite for well-informed decision-making and to support health improvement. Statistical Disclosure Control. Country of Birth -> 5 country categories 5. Statistics Netherlands is required by law to protect the privacy of its respondents as well as it can. 2012. Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata. 8 1.2.3 What disclosure risks need to be protected against? We systematically reviewed the statistical disclosure control techniques employed for releasing aggregate data in Web-based data query systems listed in the National Association for Public Health Statistics and Information Systems (NAPHSIS). Introducing readers to the R packages sdcMicro and . Unlike k-Anonymity, Download Download PDF. Phone: +31 70 337 5060. Data from statistical agencies and other institutions are mostly confidential. de Wolf, Wiley Series in Survey Methodology, ISBN 978-1-1199-7815-2; Tau Argus manual; Mu Argus manual; Manuals and software libraries are available on: https://github.com/sdcTools; Required Preparation In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Safe Outputs are only released from the Safe Setting. Introduction Traditionally, statistical agencies generally release outputs in the form of microdata and tabular data. The purpose of SDC is to protect the confidentiality of the respondents and subjects of the research. This is the combined homepage of the several European projects in the field of Statistical Disclosure Control. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data. Peter-Paul de Wolf. In summary: If a national count is between one and seven, no sub-national breakdown will be . There is a large literature base now established on disclosure risk, disclosure control and its methodology, notably Hundepool et al (2012). Full PDF Package Download Full PDF Package . Techniques of statistical disclosure control are aimed at protecting the confidentiality of individual respon-dents to a set of statistical publications. 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