Genetic algorithm-based clustering approach for k-anonymization

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.