Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation

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.