(l, e)-Diversity – A Privacy Preserving Model to Resist Semantic Similarity Attack

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 l -diversity.