A measure of variance for hierarchical nominal attributes
Information Sciences: an International Journal
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
Identity in the Information Society
Pattern-guided data anonymization and clustering
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
On sketch based anonymization that satisfies differential privacy model
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
More than modelling and hiding: towards a comprehensive view of Web mining and privacy
Data Mining and Knowledge Discovery
Microaggregation- and permutation-based anonymization of movement data
Information Sciences: an International Journal
Improvements on a privacy-protection algorithm for DNA sequences with generalization lattices
Computer Methods and Programs in Biomedicine
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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k-Anonymity is a privacy property requiring that all combinations of key attributes in a database be repeated at least for k records. It has been shown that k-anonymity alone does not always ensure privacy. A number of sophistications of k-anonymity have been proposed, like p-sensitive k-anonymity, l-diversity and t-closeness. This paper explores the shortcomings of those properties, none of which turns out to be completely convincing.