Statistical Disclosure Control (sdcMicro)

SCREENSHOTS

SDCMicro is free, R-based open-source package for the generation of protected microdata for researchers and public use. Data from statistical agencies and other institutions are mostly confidential. This package can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. In addition, various risk estimation methods are included. The associated package sdcMicroGUI includes a graphical user interface for various methods in the sdcMicro package.

Authors

Templ M, Kowarik A, Meindl B (2015). “Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro.” Journal of Statistical Software, 67(4), 1–36. doi: 10.18637/jss.v067.i04

Maintainer

Matthias Templ (matthias.templ at gmail.com)

Features

R-based free, open-source package distributed under GPL 2.0 license	Graphical user interface available for users with no or limited knowledge of the R language
Multiple options for reducing disclosure risk and for assessing information loss	Multiple methods for assessing the re-identification risk (k-anonymity, individual, and global re-identification risk)

System Requirements

sdcMicro requires a PC running MS-Windows XP, Windows Vista, Windows 7, Windows 8, Mac OS X or Unix/Linux.

Recent Developments

Funding from the World Bank and DFID has been used to fund the development of a new Graphic User Interface for the open-source R package sdcMicro. For users comfortable with using R, the package sdcMicro provides a tool for the application of a comprehensive suite of methods commonly used and described in the literature on disclosure control. Users not familiar with R, but who have an immediate need for tools to anonymize microdata, would benefit from a friendly Graphic User Interface (GUI) for the sdcMicro package. To provide a GUI environment for the non-R user, a Shiny application called sdcApp has been added to the sdcMicro package. Users of sdcApp are able to implement the most widely used anonymization methods present in the sdcMicro package without requiring in-depth knowledge of R. In addition to the anonymization methods implemented in the sdcMicro package, sdcApp offers a comprehensive set of risk and utility measures. This includes functions to measure, visualize and compare risk and utility throughout the anonymization process. sdcApp also helps users by producing reports on the methods used in formats suitable for internal and external audiences and saves the underlying R code to ensure reproducibility. For users of other statistical packages, sdcApp supports importing and exporting microdata in several formats (STATA, SAS, SPSS, CSV, R). Like R, sdcMicro is open source and available in the CRAN repositories and on GitHub.

Download

Version: 5.4.1

Date: 2018-04-13

License: GPL 2.0

Development URL: https://github.com/sdcTools/sdcMicro

The software and its documentation can be downloaded from the following link: http://cran.r-project.org/web/packages/sdcMicro/index.html.

Related Resources

Documents

Introduction to Statistical Disclosure Control (SDC)

(752.9 KB)

Author(s)

Matthias Templ, Bernhard Meindl, Alexander Kowarik and Shuang Chen

Description

This guide, Introduction to Statistical Disclosure Control (SDC), discusses common SDC methods for microdata obtained from sample surveys, censuses and administrative sources.

Date

August 2014

URL

http://ihsn.org/sites/default/files/resources/ihsn-working-paper-007-Oct27.pdf

sdcApp Reference Manual

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Author(s)

Thijs Benschop, Matthew Welch

Description

This is documentation and guidance for using sdcApp, a graphic user interface for the sdcMicro R package. sdcMicro provides tools for Statistical Disclosure Control (SDC) for microdata, also known as microdata anonymization. For an overview of the theory of SDC for microdata we suggest reading: Statistical Disclosure Control for Microdata: A Theory Guide.

Date

2019-11-12

URL

https://sdcappdocs.readthedocs.io/en/latest/

sdcMicro Reference Manual

(309.09 KB)

Author(s)

Matthias Templ, Alexander Kowarik, Bernhard Meindl

Description

Data from statistical agencies and other institutions are mostly confidential. This package can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. In addition, various risk estimation methods are included. Note that the package sdcMicroGUI includes a graphical user interface for various methods in this package.

Date

2014-07-18

URL

http://ihsn.org/sites/default/files/resources/sdcMicro.pdf

Statistical Disclosure Control for Microdata: A Practice Guide

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Author(s)

Thijs Benschop, Matthew Welch

Description

Releasing data in a safe way is required to protect the integrity of the statistical system, by ensuring agencies honor their commitment to respondents to protect their identity. Agencies do not widely share, in substantial detail, their knowledge and experience using SDC and the processes for creating safe data with other agencies. This makes it difficult for agencies new to the process to implement solutions. We consolidated knowledge from literature as well as from our own experience to inform our discussion of the processes and methods presented in this guide. This guide focuses on the implementation of methods and uses the free R based package sdcMicro for its examples. If you are interested in reading in detail about the theory behind the methods used, we suggest reading our accompanying guide: Statistical Disclosure Control for Microdata: Theory.

Date

June 2016

URL

https://sdcpractice.readthedocs.io/en/latest/

Statistical Disclosure Control for Microdata: A Practice Guide - Case Study Data and R Script

(856.35 KB)

Date

June 2016

URL

http://ihsn.org/sites/default/files/resources/case_studies_code_and_data.zip

File size

1 MB

Statistical Disclosure Control for Microdata: Theory

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Author(s)

Thijs Benschop and Matthew Welch

Description

This guide provides and introduction to the theory of Statistical Disclosure Control (SDC) for microdata. 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 (NSOs).

For guidance on the technical implementation of the theory mentioned in the guide, please refer to our guides:

- Statistical Disclosure Control for Microdata: A Practice Guide for guidance on the application of methods and on using sdcMicro from the command-line
- sdcApp manual for guidance on the application of methods and on using the GUI sdcApp available for sdcMicro

Date

11/12/2019

URL

https://sdctheory.readthedocs.io/en/latest/

Links

Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro

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Description

The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data. 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. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.

URL

http://www.jstatsoft.org/article/view/v067i04

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