P-cover k-anonymity model for protecting multiple sensitive attributes
Yingjie Wu, Xiaowen Ruan, Shangbin Liao, Xiaodong Wang · 2010
The k-anonymity model has been introduced for protecting individual privacy. While focusing on membership disclosure, k-anonymity model fail to protect sensitive attribute disclosure. Different from the existing models of single sensitive attribute, extra associations among multiple sensitive attributes should be invested. In this paper, we propose a p-cover k-anonymity model to prevent both membership and multiple sensitive attributes disclosure. We present an optimal global-recoding algorithm based on p-cover k-anonymity model. The simulation experiments on real datasets show that the proposed model and algorithm are feasible and effective.