ROCK: a robust clustering algorithm for categorical attributes
Information Systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Capturing data usefulness and privacy protection in K-anonymisation
Proceedings of the 2007 ACM symposium on Applied computing
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
An Empirical Study of Utility Measures for k-Anonymisation
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
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K-anonymisation is an approach to protecting private information contained within a dataset. Many k-anonymisation methods have been proposed recently and one class of such methods are clusteringbased. These methods are able to achieve high quality anonymisations and thus have a great application potential. However, existing clusteringbased techniques use different quality measures and employ different data grouping strategies, and their comparative quality and performance are unclear. In this paper, we present and experimentally evaluate a family of clustering-based k-anonymisation algorithms in terms of data utility, privacy protection and processing efficiency.