An Improved V-MDAV Algorithm for l-Diversity
Jianmin Han, Cen Ting-ting, YU Hui-qun · 2008
V-MDAV algorithm is a high efficient multivariate microaggregation algorithm and the anonymity table generated by the algorithm has high data quality. But it does not consider the sensitive attribute diversity, so the anonymity table generated by the algorithm cannot resist homogeneity attack and background knowledge attack. To solve the problem, the paper proposes an improved V-MDAV algorithm, which first generates groups satisfying l-diversity, then extends these groups to the size between l and 2l-1 to achieve optimal k-partition. Experimental results indicate that the algorithm can generate anonymity table satisfying sensitive attribute diversity efficiently.