One important privacy principle is that an individual has the freedom to decide his/her own privacy preferences, which should be taken into account when data holders release their privacy preserving micro data. Nevertheless, current related k-anonymity model research focuses on protecting individual private information by using pre-defined constraint parameters specified by data holders. This paper introduces a personalized (alpha, k) model by introducing a vector for describing individual personalized privacy requirements corresponding to each value in the domain of sensitive attributes by data respondents, and propose an efficiency anonymization algorithm which combines the top down specialization for quasi-identifier anonymization and the local recoding technique for the sensitive attribute generalization based on its attribute taxonomy tree. Experimental results show that this approach can meet better personalized privacy requirements and keep the information loss low.