Proceedings of the 17th International Conference on Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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
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
Acclimatizing Taxonomic Semantics for Hierarchical Content Classification
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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
Clustering-based k-anonymisation algorithms
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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k-Anonymisation is a technique for masking microdata in order to prevent individual identification. Besides preserving privacy, data anonymised by such a method must also retain its utility, i.e. it must remain useful to applications. Existing k-anonymisation methods all attempt to optimise data utility, but they do so by using measures that do not take application requirements into account. In this paper, we empirically study several popular utility measures by comparing their performance in a range of application scenarios. Our study shows that these measures may not be a reliable indicator of data utility for applications in practice, and how to use these measures effectively must be considered.