Multi-dimensional Sensitive k-anonymity Privacy Protection Model
Kai Zeng · Jisuanji gongcheng · 2012
This paper focuses on the protection problem of private information in data mining,and proposes multi-dimensional sensitive k-anonymity privacy protection model.Sensitive attribute leakage problems are divided into general leakage similar leakage,multi-dimensional independent leakage,cross leakage and multi-dimensional mixed data leakage.On the basis of k-anonymity,the similarities of multi-dimensional sensitive attribute are marked by clustering features.The model searches for anonymous records,computes the similarities between the remained records and grouped records,and generalizes the dataset satisfied to the anonymous model.The dataset is released.Experimental results show that the model is appropriate for the multi-dimensional sensitive data,and can prevent privacy leaking and have good data availability.