Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
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
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
TFRP: An efficient microaggregation algorithm for statistical disclosure control
Journal of Systems and Software
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Towards optimal k-anonymization
Data & Knowledge Engineering
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
An Efficient Microaggregation Algorithm for Mixed Data
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 03
ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Density-based microaggregation for statistical disclosure control
Expert Systems with Applications: An International Journal
k-Anonymity in the Presence of External Databases
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
An evolutionary approach to enhance data privacy
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Slicing: A New Approach for Privacy Preserving Data Publishing
IEEE Transactions on Knowledge and Data Engineering
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
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K-anonymity is a fine approach to protecting privacy in the release of microdata for data mining. Microaggregation and generalization are two typical methods to implement k-anonymity. But both of them have some defects on anonymizing mixed microdata. To address the problem, we propose a novel anonymization method, named MAGE, which can retain more semantics than generalization and microaggregation in dealing with mixed microdata. The idea of MAGE is to combine the mean vector of numerical data with the generalization values of categorical data as a clustering centroid and to use it as incarnation of the tuples in the corresponding cluster. We also propose an efficient TSCKA algorithm to anonymize mixed data. Experimental results show that MAGE can anonymize mixed microdata effectively and the TSCKA algorithm can achieve better trade-off between data quality and algorithm efficiency comparing with two well-known anonymization algorithms, Incognito and KACA.