A Personalized (α,k)-Anonymity Algorithm

In recent years, as the rapid development of Internet, data storage and computingtechnology, the process of information collection and analysis becomes more and moreconvenient, complete and accurate. However, the data publishing processes, which aim toinformation sharing, data mining, and knowledge discovery in database (KDD) and so on, areoften accompanied with the disclosure risk of sensitive privacy information. Meanwhile, theresearch of privacy preserving in the data publishing (PPDP) is presented for the reasonmentioned above, whose major target is to improve the released data security by losing someinformation in raw data appropriately under the premise of the data utility guarantee, and thento provide a very good trade-off between achieved privacy preserving and data utility.Moreover, considering the different data entities have different requirements of sensitiveinformation protection degree, the personalization service has also become a hot issue in thefield of PPDP. Finally, based on the above analysis, this paper makes deep and detailed studyon the anonymity techniques for personalization privacy preserving data publishing in thecase of ensuring the strong information utility.Firstly，a personality privacy anonymous problem orienting sensitive values is studied inthis paper. On the theoretical basis of traditional (α, k)-anonymity principle, we introduce thepersonality privacy sensitive factor, and calculate the personality privacy preservingrequirement degrees of each sensitive attribute value to realize the personality service ofsensitive values, and then give a formalized definition of the personalized (α, k)-anonymousalgorithm.This paper introduces a personalized (α, k) model by introducing a vector fordescribing individual personalized privacy requirements corresponding to each value in thedomain of sensitive attributes by data respondents, and propose an efficiency anonymizationalgorithm which combines the top-down specialization for quasi-identifier anonymization andthe local recoding technique for the sensitive attribute generalization based on its attributetaxonomy tree. Experimental results show that this approach can meet better personalizedprivacy requirements and keep the information loss low.