Uncertain Data Privacy Protection Based on K-Anonymity via Anatomy
Xiang Min Ren, Jing Yang, Jian Pei Zhang, Zong Fu Jia · Advanced engineering forum · 2012
In traditional database domain, k-anonymity is a hotspot in data publishing for privacy protection. In this paper, we study how to use k-anonymity in uncertain data set, use influence matrix of background knowledge to describe the influence degree of sensitive attribute produced by QI attributes and sensitive attribute itself, use BK(L,K)-clustering to present equivalent class with diversity, and a novel UDAK-anonymity model via anatomy is proposed for relational uncertain data. We will extend our ideas for handling how to solve privacy information leakage problem by using UDAK-anonymity algorithms in another paper.