MDAV Algorithm for Implementing (k,e)-Anonymity Model
Teng-fang Guo · Jisuanji gongcheng · 2010
MDAV(Maximum Distance to Average Vector) algorithm is an efficient microaggregation algorithm.However,it does not capture diversity of sensitive values in each equivalence class,so the anonymity table generated by the algorithm cannot resist homogeneity attack and background knowledge attack.To solve the problem,the paper proposes a(k,e)-MDAV algorithm.The algorithm groups at least the k nearest tuples to cluster center into one cluster,and further requires the range of the distinct values in one cluster to be no less than e.Experimental results show the algorithm can generate anonymity table satisfying(k,e) anonymity model efficiently.