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
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(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
Capturing data usefulness and privacy protection in K-anonymisation
Proceedings of the 2007 ACM symposium on Applied computing
A k-Anonymity Clustering Method for Effective Data Privacy Preservation
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Genetic algorithm-based clustering approach for k-anonymization
Expert Systems with Applications: An International Journal
Systematic clustering method for l-diversity model
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
A user-oriented anonymization mechanism for public data
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous 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
A sensitive attribute based clustering method for k-anonymization
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
An automated data utility clustering methodology using data constraint rules
Proceedings of the 2012 international workshop on Smart health and wellbeing
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The k-anonymity model is a privacy-preserving approach that has been extensively studied for the past few years. To minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group individually. This work proposes a clustering-based k-anonymization method that runs in O(n2/k) time. We experimentally compare our method with another clustering-based k-anonymization method recently proposed by Byun et al. Even though their method has a time complexity of O(n2), the experiments show that our method outperforms their method with respect to information loss and resilience to outliers.