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Information Loss: Evaluation and Measures

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

Part of the book series: Studies in Big Data ((SBD,volume 28))

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.

Farfar, får får får?

Nej, får får inte får,

får får vattenäpple.

Privacy-preserving Swedish proverb.

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Notes

  1. 1.

    Recall that we have discussed distances and metrics, as well as their properties in Sects. 5.4.7 and 5.6.1.

  2. 2.

    Commonality is the percentage of each variable that is explained by a principal component.

  3. 3.

    The factor scores stand for the factors that should multiply each variable in X to obtain its projection on each principal component.

  4. 4.

    Reference [34] has a similar use of the Hellinger distance for comparing tables, but for tabular data protection.

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  1. School of Informatics, University of Skövde, Skövde, Sweden

    Vicenç Torra

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Torra, V. (2017). Information Loss: Evaluation and Measures. In: Data Privacy: Foundations, New Developments and the Big Data Challenge. Studies in Big Data, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-57358-8_7

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

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