earticle

영어

The rapid development of information technology makes it convenient to release, collect, store and analyze various types of data. At the same time, how to protect the privacy of individual and prevent disclosure of sensitive information during data publication has become a major challenge. K-anonymity method is the most widely used privacy protection model and has been well researched. However, generalization and suppression operations used in K-anonymity methods require high computational effort and cause excessive loss of original information, which will greatly reduce the availability of data after publishing. The paper proposed a transformation algorithm for privacy preserving data publishing based on fuzzy semantic set pair cloud model (FSSPCM). It transforms the sensitive attributes into the form of fuzzy semantic values, and privacy of individual has been maintained because exact values cannot be predicted after data publishing. In order to enhance the availability of data after publishing, semantic distinction (SD) and reserve degree (RD) are designed to reflect relationships between original data and fuzzy semantic information after transformation according to different characteristics of numerical sensitive attributes and categorical sensitive attributes. Experiments and analysis demonstrate the effectiveness of the proposed method both on numerical and categorical sensitive attributes. Classification performed on original and transformed information proves the proposed method maintains higher clustering similarity after fuzzy transformation, which will provide better availability for data mining and other processing.