Methods for Data Perturbation

Matthias Templ ()
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Matthias Templ: Zurich University of Applied Sciences (ZHAW), Institute of Data Analysis and Process Design (IDP), School of Engineering (SoE)

Chapter Chapter 4 in Statistical Disclosure Control for Microdata, 2017, pp 99-132 from Springer

Abstract: Abstract Methods for perturbation of data differ for categorical and continuous variables. The risk for categorical key variables is dependent on the frequency counts of keys, whereas keys with only few observations are problematic. Categories of categorical key variables with low frequency counts are therefore often recoded and combined with other categories. However, a still too high disclosure risk may be present for some individuals. Local suppression is one method to further reduce the disclosure risk. In order to find a well-balanced, suitable solution, global recoding is usually applied in an explorative manner to observe with which (reasonable) recodings one achieves the best effect in terms of reducing the disclosure risk and providing high data utility. Especially with a large amount of key variables, swapping methods, such as PRAM, are good alternatives. Methods for continuous scaled variables are combining values (microaggregation) or adding noise to the values. Advanced methods such as shuffling allow to preserve certain statistics.

Keywords: Frequency Count; Local Suppression; Disclosure Risk; Noise Method; Default Method (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-50272-4_4

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