Personalized(l,c)-anonymity algorithm based on clustering
Jiandong Wang · Computer Engineering and Applications Journal · 2012
At present most l-diverse anonymity algorithms are vulnerable to similarity attack and skewness attack due to treating all sensitive attribute values equally and without considering the sensitivity and specific distribution.Moreover,these algorithms result in high information loss on account of performing full domain generalization to create equivalence class.This paper proposes a personalized(l,c)-anonymity algorithm based on clustering,which improves the security through defining sensitivity for different sensitive attribute value and maximal ratio threshold and reduces information loss via clustering technique.Theoretical analysis and experimental results indicate that the method is effective and feasible.