20 20

Transactions on
Data Privacy
Foundations and Technologies

http://www.tdp.cat


Articles in Press

Accepted articles here

Latest Issues

Year 2024

Volume 17 Issue 1

Year 2023

Volume 16 Issue 3
Volume 16 Issue 2
Volume 16 Issue 1

Year 2022

Volume 15 Issue 3
Volume 15 Issue 2
Volume 15 Issue 1

Year 2021

Volume 14 Issue 3
Volume 14 Issue 2
Volume 14 Issue 1

Year 2020

Volume 13 Issue 3
Volume 13 Issue 2
Volume 13 Issue 1

Year 2019

Volume 12 Issue 3
Volume 12 Issue 2
Volume 12 Issue 1

Year 2018

Volume 11 Issue 3
Volume 11 Issue 2
Volume 11 Issue 1

Year 2017

Volume 10 Issue 3
Volume 10 Issue 2
Volume 10 Issue 1

Year 2016

Volume 9 Issue 3
Volume 9 Issue 2
Volume 9 Issue 1

Year 2015

Volume 8 Issue 3
Volume 8 Issue 2
Volume 8 Issue 1

Year 2014

Volume 7 Issue 3
Volume 7 Issue 2
Volume 7 Issue 1

Year 2013

Volume 6 Issue 3
Volume 6 Issue 2
Volume 6 Issue 1

Year 2012

Volume 5 Issue 3
Volume 5 Issue 2
Volume 5 Issue 1

Year 2011

Volume 4 Issue 3
Volume 4 Issue 2
Volume 4 Issue 1

Year 2010

Volume 3 Issue 3
Volume 3 Issue 2
Volume 3 Issue 1

Year 2009

Volume 2 Issue 3
Volume 2 Issue 2
Volume 2 Issue 1

Year 2008

Volume 1 Issue 3
Volume 1 Issue 2
Volume 1 Issue 1


Volume 1 Issue 2


Statistical Disclosure Control for Microdata Using the R-Package sdcMicro

Matthias Templ(a),(b),(*)

Transactions on Data Privacy 1:2 (2008) 67 - 85

Abstract, PDF

(a) Department of Methodology; Statistics Austria; Guglgasse 13; 1110 Vienna; Austria. e-mail: matthias.templ@statistik.gv.at

(b) Department of Statistics and Probability Theory; Vienna University of Technology; Wieder Hauptstr. 8-10; 1040 Vienna; Austria. e-mail: templ@tuwien.ac.at


Abstract

The demand for high quality microdata for analytical purposes has grown rapidly among researchers and the public over the last few years. In order to respect existing laws on data privacy and to be able to provide microdata to researchers and the public, statistical institutes, agencies and other institutions may provide masked data. Using our flexible software tools with which one can apply protection methods in an exploratory manner, it is possible to generate high quality confidential (micro-)data.

In this paper we present highly flexible and easy to use software for the generation of anonymized microdata and give insights into the implementation and the design of the R-Package sdcMicro. R is a highly extendable system for statistical computing and graphics, distributed over the net. sdcMicro contains almost all popular methods for the anonymization of both categorical and continuous variables. Furthermore, several new methods have been implemented. The package can also be used for the comparison of methods and for measuring the information loss and disclosure risk of the masked data.

* Corresponding author.

Follow us




Supports



ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; Vicenç Torra; Umeå University; 90187 Umeå (Sweden); e-mail:tdp@tdp.cat
Note: TDP's web site does not use cookies. TDP does not keep information neither on IP addresses nor browsers. For the privacy policy access here.

 


Vicenç Torra, Last modified: 00 : 25 December 12 2014.