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 kanonymization) 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.