Research On Privacy-preserving Method With Independent L-diversity For Classification

With the development and popularization of information technology, a lot of personal information is published to be used for data mining, however, it will threat the privacy of individuals’ with providing people a powerful knowledge and profit. Therefore, the study of new and practical privacy protection technology which aims at the balance between privacy and utility of data is meaningful.For problems that some privacy protection technologys for classification exist the problem of inadequate security, and the existing privacy protection technologys which meet the principle of Independent L-Diversity do not apply to classification, a Privacy-Preserving method with Independent L-Diversity for Classification is proposed. The method has three options: the initialized option is done to improve the efficiency of the algorithm, the partition option based on the method of Top-Down Specialization has two layer division,through the option of Cut Node choosing, we will obtain more information gain and reduce the information loss caused by the generalization, by the way, after the first layer division, each tuple in divided groups will be added noisy values of SA, and the distorb option is done to make the anonymous data meet the principle of independence L-diversity.The anonymous data is proved that it can be used in classify mining applications, and satisfies the principle of Independent L-Diversity by the analysis of algorithm. Finally, results of characteristics of the experiment and related algorithms comparative experiments, show that the algorithm can be used in classification, and it can preserve good utilities in classification with high security.