Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards Balancing Data Usefulness and Privacy Protection in K-Anonymisation
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Data utility and privacy protection trade-off in k-anonymisation
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
An efficient clustering method for k-anonymization
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
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
Does enforcing anonymity mean decreasing data usefulness?
Proceedings of the 4th ACM workshop on Quality of protection
On the comparison of microdata disclosure control algorithms
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Genetic algorithm-based clustering approach for k-anonymization
Expert Systems with Applications: An International Journal
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
A multi-objective approach to data sharing with privacy constraints and preference based objectives
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
POkA: identifying pareto-optimal k-anonymous nodes in a domain hierarchy lattice
Proceedings of the 18th ACM conference on Information and knowledge management
Speeding up clustering-based k-anonymisation algorithms with pre-partitioning
BNCOD'07 Proceedings of the 24th British national conference on Databases
Systematic clustering method for l-diversity model
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Preventing range disclosure in k-anonymised data
Expert Systems with Applications: An International Journal
PCTA: privacy-constrained clustering-based transaction data anonymization
Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society
An efficient clustering algorithm for k-anonymisation
Journal of Computer Science and Technology
Utility-guided Clustering-based Transaction Data Anonymization
Transactions on Data Privacy
A sensitive attribute based clustering method for k-anonymization
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
An automated data utility clustering methodology using data constraint rules
Proceedings of the 2012 international workshop on Smart health and wellbeing
Clustering-based k-anonymisation algorithms
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Journal of Computer Security
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K-anonymisation is an approach to protecting privacy contained within a data set. A good k-anonymisation algorithm should anonymise a data set in such a way that private information contained within it is hidden, yet anonymised data is still useful in intended applications. Maximising both data usefulness and privacy protection in k-anonymisation is however difficult. In this paper, we suggest a metric that attempts to quantify these two properties and introduce a clustering based algorithm that can achieve a balance between them in k-anonymisation.