Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A practice-oriented framework for measuring privacy and utility in data sanitization systems
Proceedings of the 2010 EDBT/ICDT Workshops
k-anonymization without Q-S associations
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
Can the Utility of Anonymized Data be Used for Privacy Breaches?
ACM Transactions on Knowledge Discovery from Data (TKDD)
Secure distributed computation of anonymized views of shared databases
ACM Transactions on Database Systems (TODS)
Clustering-based k-anonymisation algorithms
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Incorporating Privacy into the Undergraduate Curriculum
Proceedings of the 2013 on InfoSecCD '13: Information Security Curriculum Development Conference
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k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced to an individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. To achieve optimal and practical k-anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. This paper presents the family of clustering based algorithms that are more flexible and even attempts to improve precision by ignoring the restrictions of user defined Domain Generalization Hierarchies. The main finding of the paper will be that metrics may behave differently through different algorithms and may not show correlations with some applications' accuracy on output data.