Putting Statistical Disclosure Control into Practice: The ARX Data Anonymization Tool

2015 ◽  
pp. 111-148 ◽  
Author(s):  
Fabian Prasser ◽  
Florian Kohlmayer
Keyword(s):  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Data Anonymization ◽  
Statistical Disclosure

scholarly journals Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 191
Author(s):  
Bernhard Meindl ◽  
Matthias Templ
Keyword(s):  
Statistical Disclosure Control ◽  
Software Environment ◽  
Web Based ◽  
Disclosure Control ◽  
Data Anonymization ◽  
Know How ◽  
Whole Process ◽  
Statistical Disclosure ◽  
Anonymized Data ◽  
The Impact

The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app.

Density-based microaggregation for statistical disclosure control

2010 ◽  
Vol 37 (4) ◽  
pp. 3256-3263 ◽  
Author(s):  
Jun-Lin Lin ◽  
Tsung-Hsien Wen ◽  
Jui-Chien Hsieh ◽  
Pei-Chann Chang
Keyword(s):  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Statistical Disclosure

scholarly journals Statistical Disclosure Control Methods for Microdata from the Labour Force Survey

2020 ◽  
Vol 3 (348) ◽  
pp. 7-24
Author(s):  
Michał Pietrzak
Keyword(s):  
Labour Force ◽  
Control Methods ◽  
Labour Force Survey ◽  
Statistical Disclosure Control ◽  
Disclosure Control ◽  
Original Dataset ◽  
Level Data ◽  
Statistical Disclosure ◽  
The Impact

The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey. In the first step, the author assessed to what extent the confidentiality of information was protected in the original dataset. In the second step, after applying selected methods implemented in the sdcMicro package in the R programme, the impact of those methods on the disclosure risk, the loss of information and the quality of estimation of population quantities was assessed. The conclusion highlights some problematic aspects of the use of Statistical Disclosure Control methods which were observed during the conducted analysis.

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