Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
A crossover operator for the k- anonymity problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
(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
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
(t, λ)-Uniqueness: Anonymity Management for Data Publication
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
On-the-fly generalization hierarchies for numerical attributes revisited
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
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We present in this paper a method for dynamically creating hierarchies for quasi-identifier numerical attributes. The resulting hierarchies can be used for generalization in microdata k-anonymization, or for allowing users to define generalization boundaries for constrained k-anonymity. The construction of a new numerical hierarchy for a numerical attribute is performed as a hierarchical agglomerative clustering of that attribute's values in the dataset to anonymize. Therefore, the resulting tree hierarchy reflects well the closeness and clustering tendency of the attribute's values in the dataset. Due to this characteristic of the hierarchies created on-the-fly for quasi-identifier numerical attributes, the quality of the microdata anonymized through generalization based on these hierarchies is well preserved, and the information loss in the anonymization process remains in reasonable bounds, as proved experimentally.