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
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient clustering method for k-anonymization
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
BSGI: An Effective Algorithm towards Stronger l-Diversity
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
IEEE Transactions on Knowledge and Data Engineering
A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation
ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
On guaranteeing k-anonymity in location databases
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
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Privacy preservation (PP) has become an important issue in the information age to prevent expositions and abuses of personal information. This has attracted much research and k-anonymity is a well-known and promising model invented for PP. Based on the k-anonymity model, this paper introduces a novel and efficient member migration algorithm, called eM2, to ensure k-anonymity and avoid information loss as much as possible, which is the crucial weakness of the model. In eM2, we do not use the existing generalization and suppression technique. Instead we propose a member migration technique that inherits advantages and avoids disadvantages of existing k-anonymity-based techniques. Experimental results with real-world datasets show that eM2 is superior to other k-anonymity algorithms by an order of magnitude.