CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Dynamic multidimensional histograms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Maxdiff kd-trees for data condensation
Pattern Recognition Letters
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
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
An efficient clustering algorithm for k-anonymisation
Journal of Computer Science and Technology
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K-anonymisation is a technique for protecting privacy contained within a dataset. Many k-anonymisation algorithms have been proposed, and one class of such algorithms are clustering-based. These algorithms can offer high quality solutions, but are rather inefficient to execute. In this paper, we propose a method that partitions a dataset into groups first and then clusters the data within each group for k-anonymisation. Our experiments show that combining partitioning with clustering can improve the performance of clustering-based kanonymisation algorithms significantly while maintaining the quality of anonymisations they produce.