A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
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
Capturing data usefulness and privacy protection in K-anonymisation
Proceedings of the 2007 ACM symposium on Applied computing
An overview of clustering methods
Intelligent Data Analysis
An efficient clustering method for k-anonymization
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
A Hybrid Method for k-Anonymization
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Privacy preserving itemset mining through noisy items
Expert Systems with Applications: An International Journal
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Comparison of microaggregation approaches on anonymized data quality
Expert Systems with Applications: An International Journal
Time complexity estimation and optimisation of the genetic algorithm clustering method
WSEAS Transactions on Mathematics
A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
k-Anonymity has been widely adopted as a model for protecting public released microdata from individual identification. This model requires that each record must be identical to at least k-1 other records in the anonymized dataset with respect to a set of privacy-related attributes. Although anonymizing the original dataset to satisfy the requirement of k-anonymity is easy, the anonymized dataset must preserve as much information as possible of the original dataset. Clustering techniques have recently been successfully adapted for k-anonymization. This work proposes a novel genetic algorithm-based clustering approach for k-anonymization. The proposed approach adopts various heuristics to select genes for crossover operations. Experimental results show that this approach can further reduce the information loss caused by traditional clustering-based k-anonymization techniques.