(k, ε)-Anonymity: An anonymity model for thwarting similarity attack
Haiyuan Wang, Jianmin Han, Jiyi Wang, Lixia Wang · 2013
Existing anonymity models rarely consider the semantic similarity between sensitive values, so they cannot thwart similarity attack. To solve the problem, this paper proposes a (k, ε)-anonymity model which requires that each equivalence class in anonymous dataset satisfy k-anonymity constraints. At the same time, any two sensitive values in the same equivalence class are not ε-similar. The paper also proposes a (k, ε)-KACA algorithm. Experimental results show that the anonymous data satisfy(k, ε)-anonymity has higher diversity than that satisfy k-anonymity model, so (k, ε)-anonymity model can protect privacy more effective than k-anonymity model.