(L, m, d) \#x2014; Anonymity : A Resisting Similarity Attack Model for Multiple Sensitive Attributes

Preserving privacy is extremely important in data publishing. The existing privacy-preserving models are mostly oriented to single sensitive attribute, can not be applied to multiple sensitive attributes situation. Moreover, they do not consider the semantic similarity between sensitive attribute values, and may be vulnerable to similarity attack. In this paper, we propose a (l, m, d)-anonymity model for multiple sensitive attributes similarity attack, where m is the dimension of the sensitive attributes. This model uses the semantic hierarchical tree to analyze and compute the semantic dissimilarity between sensitive attribute values, and each equivalence class must exist at least l sensitive attribute values that satisfy d-different on each dimension sensitive attribute. Meanwhile, in order to make the published data highly available, our model adopts the distance-based measurement method to divide the equivalence class. We carry out extensive experiments to certify the (1, m, d)-anonymity model can significantly reduce the probability of sensitive information leakage and protect individual privacy more effectively.