Full autonomy: A novel individualized anonymity model for privacy preserving

Abstract

An important principle in privacy preservation is individualized privacy autonomy which means individual has the freedom to decide and choose privacy constraints. Currently, many individualized anonymous models which have been proposed unite privacy autonomy, and most of the individualized models are focused on autonomy of sensitive attributes. As the autonomy of quasi-identifier (QI) attributes are neglected, it is unfully autonomous showed in these individualized models. In order to achieve full privacy autonomy, an individualized (α, ω)-anonymity model is proposed in this paper, where α and ω which respectively represent the constraint value of the sensitive attributes and the QI attributes are both set by providers. The model doesn't need to set the constraint value k. Furthermore, it is combined with the granular computing and the top–down local recoding to process the providers' datasets in different intervals and then to achieve differential protection for different granular spaces. Moreover, the performance analysis shows that this model not only satisfies the individualized privacy requirements, but also brings higher efficiency and lower information loss.