A Personalized Extended (a, k)-Anonymity Model

On the schemes of personalized privacy preservation, the sensitive attribute value-oriented anonymous method can not satisfy the different privacy preservation requirements for each individual. Therefore we present a personalized extended (α, k)-anonymity model based on clustering techniques. The model can not only avoid privacy disclosure caused by the occurrence imbalance of sensitive attribute values but also fulfill the privacy preservation requirements for individuals, and realizes the combination of sensitive value-oriented privacy preservation method and individual-oriented method. Experimental results show that the personalized extended (α, k)-anonymity model can provide stronger privacy protection efficiently.