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Data Mining and Knowledge Discovery

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01-09-2005

Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata

Authors: Josep M. Mateo-Sanz, Josep Domingo-Ferrer, Francesc Sebé

Published in: Data Mining and Knowledge Discovery | Issue 2/2005

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Abstract

Inference control for protecting the privacy of microdata (individual data) should try to optimize the tradeoff between data utility (low information loss) and protection against disclosure (low disclosure risk). Whereas risk measures are bounded between 0 and 1, information loss measures proposed in the literature for continuous data are unbounded, which makes it awkward to trade off information loss for disclosure risk. We propose in this paper to use probabilities to define bounded information loss measures for continuous microdata.

previous article Preserving the Confidentiality of Categorical Statistical Data Bases When Releasing Information for Association Rules*

next article Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation

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Title: Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata
Authors: Josep M. Mateo-Sanz
Josep Domingo-Ferrer
Francesc Sebé
Publication date: 01-09-2005
Publisher: Springer US
Published in: Data Mining and Knowledge Discovery / Issue 2/2005
Print ISSN: 1384-5810
Electronic ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-005-0011-9

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