Data Utility and Information Loss

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 5 in Statistical Disclosure Control for Microdata, 2017, pp 133-156 from Springer

Abstract: Abstract Once SDC methods have been applied to modify the original data set and to lower the disclosure risk, it is critical to measure the resulting information loss and data utility. Basically, two different kinds of complementary approaches exist to assess information loss: (i) direct measuring of distances/frequencies between the original data and perturbed data, and (ii) comparing statistics computed on the original and perturbed data. The first concept is common but often of limited use. The latter concept is closer to the users and data sets since its aim is to measure the differences for the most important indicators/estimates.

Keywords: Propensity Score; Gini Coefficient; Data Utility; Utility Measure; Local Suppression (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-50272-4_5

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