Ordinal,Continuous and Heterogeneous <Emphasis Type="Italic">k</Emphasis>-Anonymity Through Microaggregation |
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Authors: | Email author" target="_blank">Josep?Domingo-FerrerEmail author Vicen??Torra |
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Affiliation: | 1.Department of Computer Engineering and Maths,Rovira i Virgili University of Tarragona,Tarragona,Spain;2.Institut d'Investigació en Intel·ligència Artificial-CSIC,Bellaterra,Spain |
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Abstract: | k-Anonymity is a useful concept to solve the tension between data utility and respondent privacy in individual data (microdata)
protection. However, the generalization and suppression approach proposed in the literature to achieve k-anonymity is not equally suited for all types of attributes: (i) generalization/suppression is one of the few possibilities
for nominal categorical attributes; (ii) it is just one possibility for ordinal categorical attributes which does not always
preserve ordinality; (iii) and it is completely unsuitable for continuous attributes, as it causes them to lose their numerical
meaning. Since attributes leading to disclosure (and thus needing k-anonymization) may be nominal, ordinal and also continuous, it is important to devise k-anonymization procedures which preserve the semantics of each attribute type as much as possible. We propose in this paper
to use categorical microaggregation as an alternative to generalization/suppression for nominal and ordinal k-anonymization; we also propose continuous microaggregation as the method for continuous k-anonymization.
Editor: Geoff Webb |
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Keywords: | k-anonymity microdata privacy database security microaggregation |
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