The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
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)
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Maxdiff kd-trees for data condensation
Pattern Recognition Letters
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
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
Does enforcing anonymity mean decreasing data usefulness?
Proceedings of the 4th ACM workshop on Quality of protection
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Preventing range disclosure in k-anonymised data
Expert Systems with Applications: An International Journal
Privacy consensus in anonymization systems via game theory
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
On location privacy and quality of information in participatory sensing
Proceedings of the 8h ACM symposium on QoS and security for wireless and mobile networks
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K-anonymisation is an approach to protecting privacy contained within a dataset. A good k-anonymisation algorithm should anonymise a dataset in such a way that private information contained within it is hidden, yet the anonymised data is still useful in intended applications. However, maximising both data utility and privacy protection in k-anonymisation is not possible. Existing methods derive k-anonymisations by trying to maximise utility while satisfying a required level of protection. In this paper, we propose a method that attempts to optimise the trade-off between utility and protection. We introduce a measure that captures both utility and protection, and an algorithm that exploits this measure using a combination of clustering and partitioning techniques. Our experiments show that the proposed method is capable of producing k-anonymisations with required utility and protection trade-off and with a performance scalable to large datasets.