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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

7. Information Loss: Evaluation and Measures

verfasst von : Vicenç Torra

Erschienen in: Data Privacy: Foundations, New Developments and the Big Data Challenge

Verlag: Springer International Publishing

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Abstract

Masking methods modify databases in order to avoid disclosure. This causes some information loss that can be quantified. In this chapter we discuss different alternatives to evaluate in what extent relevant information is lost. We give an overview of generic and specific information loss measures.

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Vorheriges Kapitel Masking Methods
Nächstes Kapitel Selection of Masking Methods
Fußnoten
1
Recall that we have discussed distances and metrics, as well as their properties in Sects. 5.​4.​7 and 5.​6.​1.
 
2
Commonality is the percentage of each variable that is explained by a principal component.
 
3
The factor scores stand for the factors that should multiply each variable in X to obtain its projection on each principal component.
 
4
Reference [34] has a similar use of the Hellinger distance for comparing tables, but for tabular data protection.
 
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Metadaten
Titel
Information Loss: Evaluation and Measures
verfasst von
Vicenç Torra
Copyright-Jahr
2017
Buch
Data Privacy: Foundations, New Developments and the Big Data Challenge

Print ISBN: 978-3-319-57356-4

Electronic ISBN: 978-3-319-57358-8

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-57358-8_7