\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Information Sciences: an International Journal
Distance functions for categorical and mixed variables
Pattern Recognition Letters
Consistent models of transitivity for reciprocal preferences on a finite ordinal scale
Information Sciences: an International Journal
A Critique of k-Anonymity and Some of Its Enhancements
ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Stochastic dominance-based rough set model for ordinal classification
Information Sciences: an International Journal
Regression for ordinal variables without underlying continuous variables
Information Sciences: an International Journal
Generating microdata with p-sensitive k-anonymity property
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
C-safety: a framework for the anonymization of semantic trajectories
Transactions on Data Privacy
Information Sciences: an International Journal
n-confusion: a generalization of k-anonymity
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Information Sciences: an International Journal
Anonymization methods for taxonomic microdata
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Marginality: a numerical mapping for enhanced exploitation of taxonomic attributes
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Role Mining with Probabilistic Models
ACM Transactions on Information and System Security (TISSEC)
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The need for measuring the dispersion of nominal categorical attributes appears in several applications, like clustering or data anonymization. For a nominal attribute whose categories can be hierarchically classified, a measure of the variance of a sample drawn from that attribute is proposed which takes the attribute's hierarchy into account. The new measure is the reciprocal of ''consanguinity'': the less related the nominal categories in the sample, the higher the measured variance. For non-hierarchical nominal attributes, the proposed measure yields results consistent with previous diversity indicators. Applications of the new nominal variance measure to economic diversity measurement and data anonymization are also discussed.