Anatomy: Uncertain Data <i>k</i>-Anonymity Privacy Protection Algorithm

Abstract

Uncertain data management has become an important research direction and a hot area of research. This paper proposes an UDAK-anonymity algorithm via anatomy for relational uncertain data. Uncertain data influence matrix based on background knowledge is built in order to describe the influence degree of sensitive attribute and Quasi-identifier (QI) attributes. We use generalization and BK(L,K)-clustering to present equivalent class, L makes sensitive attributes diversity in one equivalent class. Experimental results show that UDAK-anonymity algorithm are utility, effective and efficient, and can make anonymous uncertainty data effectively resist background knowledge attack and homogeneity attack.