Personalized sensitive attribute anonymity based on P - sensitive k anonymity
Junjie Jia, Guo-lei Yan, Li-cheng Xing · 2016
With the development of science and technology, privacy protection has also been highly valued. Existing anonymity algorithms are only anonymous quasi-identifier to achieve privacy protection, but ignore the sensitive properties of the personalized protection. This paper proposed an anonymity algorithm based on p -sensitive k anonymity model, which can better protect sensitive attribute according to individual differences. The algorithm presented an innovative idea that user-defined sensitivity levels of sensitive attributes, at first users defined the sensitivity level according to their actual situation, and then used sensitive attribute level tree to generalize sensitive attribute, the high sensitivity of sensitive attribute was generalized to the higher level of the tree andvice versa. Compared with theresults of k anonymity and personalized (∂, k) anonymity, it is shows that the model based on p -sensitive k anonymity relative to other anonymous algorithms under shorter execution time and less information loss realizesthe personalized anonymity of sensitive attribute.