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- Publisher
- Springer Journals
- Copyright
- Copyright © 2005 by Springer Science+Business Media, Inc.
- Subject
- Computer Science; Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
- ISSN
- 1384-5810
- eISSN
- 1573-756X
- DOI
- 10.1007/s10618-005-0007-5
- Publisher site
- See Article on Publisher Site
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.
Journal
Data Mining and Knowledge Discovery – Springer Journals
Published: Aug 23, 2005