Research on K anonymity algorithm based on association analysis of data utility

More and more medical data are shared, which leads to disclosure of personal privacy information. Therefore, the construction of medical data privacy preserving publishing model is of great value: not only to make a non-correspondence between the released information and personal identity, but also to maintain the data utility after anonymity. However, there is an inherent contradiction between the anonymity and the data utility. In this paper, a Principal Component Analysis-Grey Relational Analysis (PCA-GRA) K anonymous algorithm is proposed to improve the data utility effectively under the premise of anonymity, in which the association between quasi-identifiers and the sensitive information is reckoned as a criterion to control the generalization hierarchy. Compared with the previous anonymity algorithms, results show that the proposed PCA-GRA K anonymous algorithm has achieved significant improvement in data utility from three aspects, namely information loss, feature maintenance and classification evaluation performance.