Clustering-based sensitive attribute -diversity anonymization algorithms
Cheng Zhong · Jisuanji gongcheng yu sheji · 2010
Two clustering-based sensitive attribute-diversity anonymization algorithms are presented.The algorithms generate the clusters which have at least distinct values of sensitive attributes.The size of each cluster is between and 2-1 to achieve the optimal partition and to improve the security of the data.The algorithms also generate the candidate tuples to reduce the unnecessary computation and the comparison operations,and always select the tuple that has minimal information loss to cluster centroid to generate the clusters,and improve the algorithm efficiency and reduce the information loss.The experimental results show that the presented algorithms are efficient and the generated anonymity table has high utility.