¹Department of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, Zhejiang, PRC
²Xingzhi College, Zhejiang Normal University, Jinhua, 321004, Zhejiang, PRC

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Abstract—Existing sensitive attributes diversity models do not capture the semantic similarity between sensitive values, so they cannot resist semantic similarity attack. To address the problem, we present a method to measure semantic similarity of a categorical sensitive attribute based on the attribute’ semantic hierarchy tree. On basis of the measurement, the paper proposes a (l, e)-diversity model which has two constraints in each equivalence class: (1) there are at least l well-represented values; (2) any two sensitive values are not e-similar. Furthermore, the paper designs a liner-complexity maximum bucketization greedy algorithm to implement the model. Experimental results show that the anonymous data satisfied (l, e)-diversity has a higher diversity degree than that satisfied l-diversity, so (l, e)-diversity can protect privacy more effectively than ldiversity.

Index Terms—Data privacy, l-diversity, (l, e)-diversity, anatomy, semantic similarity attack

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Cite: Haiyuan Wang, Jianmin Han, Jiyi Wang, Lixia Wang, "(l, e)-Diversity – A Privacy Preserving Model to Resist Semantic Similarity Attack," Journal of Computers vol. 9, no. 1, pp. 59-64, 2014.