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    Ge Weiping, Wang Wei, Zhou Haofeng, and Shi Baile. Privacy Preserving Classification Mining[J]. Journal of Computer Research and Development, 2006, 43(1): 39-45.
    Citation: Ge Weiping, Wang Wei, Zhou Haofeng, and Shi Baile. Privacy Preserving Classification Mining[J]. Journal of Computer Research and Development, 2006, 43(1): 39-45.

    Privacy Preserving Classification Mining

    • Privacy preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix a novel privacy preserving classification mining algorithm is proposed, which suits non-char type data (Boolean, categorical, and numeric type) and non-uniform probability distribution of original data, and can perturb label attribute. Experimental results demonstrate that the decision tree built using this algorithm on perturbed data has a classifying accuracy comparable to that of the decision tree built using un-privacy-preserving algorithm on original data.
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