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  l -diversity.
['Haiyuan Wang', 'Jianmin Han', 'Jiyi Wang', 'Lixia Wang']
(l, e)-Diversity – A Privacy Preserving Model to Resist Semantic Similarity Attack