Affiliations: [a] Department of Economics, Carl von Ossietzky University Oldenburg, Oldenburg, Germany | [b] Faculty of Economics, Vasile Goldiş Western University Arad, Arad, Romania
Correspondence:
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Corresponding author: Ralf Stecking, Department of Economics, Carl von Ossietzky University Oldenburg, Ammerländer Heerstr. 114-118, D-26111 Oldenburg, Germany. Tel.: +49 0441 798 4840; Fax: +49 0441 798 4116; E-mail:ralf.w.stecking@uni-oldenburg.de
Abstract: Modern data collections create vast opportunities for detecting useful hidden
relationships. Also, increasingly, they fuel data privacy concerns. A trade-off between privacy
protection and data usefulness is by now widely acknowledged.
Real world data classification tasks, as for example credit scoring applications
have to deal with such data security limitations by finding a way to effectively incorporate
privacy preserving procedures. To this end we propose as a first stage to use a
microaggregation procedure in order to anonymize data over personal credit client
feature information. In a second stage we examine the performance of support vector machines (SVM)
on such anonymized data. SVM are powerful and robust machine learning methods,
having superior credit scoring classification performance when applied to original, non-anonymized
data. We first partition the original credit scoring data set and construct anonymized
data representatives, which are then used for credit client behavior
forecasting models constructed by SVM and other comparable learning methods.
The validation procedure for such models is adapted to the two-stage modeling
approach.
In order to assess the loss owing to data anonymization, the different classification models
are evaluated against models that are trained on the original data.
Keywords: Data privacy, microaggregation, credit scoring, support vector machines
