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Genetic algorithm-based clustering approach for k-anonymization
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DOI：10.1016/j.eswa.2009.02.009.png)
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摘要
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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. (C) 2009 Elsevier Ltd. All rights reserved.
Keyword:
k-Anonymity
Clustering
Genetic algorithm
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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