Auflistung nach Autor:in "Meindl, Bernhard"

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Feedback-based integration of the whole process of data anonymization in a graphical interface
(MDPI, 2019) Meindl, Bernhard; Templ, Matthias
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.
01A - Beitrag in wissenschaftlicher Zeitschrift
Simulation of synthetic complex data. The R package simPop
(UCLA, Dept. of Statistics, 2017) Templ, Matthias; Meindl, Bernhard; Kowarik, Alexander; Dupriez, Olivier
The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.
01A - Beitrag in wissenschaftlicher Zeitschrift
State-of-the-art of Laeken Indicators. Research Project Report WP1, D1.1, FP7-SSH-2007-217322 AMELI
(01.03.2011) Monique, Graf; Alfons, Andreas; Bruch, Christian; Filzmoser, Peter; Hulliger, Beat; Lehtonen, Risto; Meindl, Bernhard; Münnich, Ralf; Schoch, Tobias; Templ, Matthias; Valaste, Maria; Wenger, Ariane; Zins, Stefan
05 - Forschungs- oder Arbeitsbericht