Computer Science ›› 2017, Vol. 44 ›› Issue (7): 42-46.doi: 10.11896/j.issn.1002-137X.2017.07.008

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Classification Anonymity Algorithm Based on Weight Attributes Entropy

LIAO Jun, JIANG Chao-hui, GUO Chun and PING Yuan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to efficiently protect data privacy being not leaked,which have high availability,a classification anony-mous method based on weight attributes entropy(WECA) was proposed.The method builds on application-specific background of data classification mining,and calculates the classification importance of different standard identifier to sensitive attribute by the concept of information entropy in the data set,which selects the highest ratio of weight attribu-tes entropy in classification quasi-identifier attributes to favorably divide the classification tree.The method also constructs the anonymous information loss measures of classification,which ensures the utility of classification on the premise of protecting privacy data.Finally,the experimental results on the standard data set show that the algorithm has fewer anonymous losses and higher classification accuracy,improving data availability.

Key words: Privacy protection,Classification anonymous,Weight attributes entropy,Classification accuracy

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