K-anonymization incremental maintenance and optimization techniques

Authors:
Traian Marius Truta;Alina Campan
Affiliations:
Northern Kentucky University, Highland Heights, KY;Babes-Bolyai University, Cluj-Napoca, RO, Romania
Venue:
Proceedings of the 2007 ACM symposium on Applied computing
Year:
2007

Citing 15
Cited 16

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Abstract

New privacy regulations together with ever increasing data availability and computational power have created a huge interest in data privacy research. One major research direction is built around k-anonymity property, which is required for the released data. Although many k-anonymization algorithms exist for static data, a complete framework to cope with data evolution (a real world scenario) has not been proposed before. In this paper, we introduce algorithms for the maintenance of k-anonymized versions of large evolving datasets. These algorithms incrementally manage insert/delete/update dataset modifications. Our results showed that incremental maintenance is very efficient compared with existing techniques and preserves data quality. The second main contribution of this paper is an optimization algorithm that is able to improve the quality of the solutions attained by either the non-incremental or incremental algorithms.