Data prediction classification method and device

Abstract

The invention discloses a data prediction classification method and device relating to the data process technique field, solving the problem in the prior art that classification result itself and classification count value are possible to leak the private information of a user. The method comprises: building a random forest namely multiple decision trees through a training dataset; carrying out prediction classification to a test dataset by the decision trees in the random forest, and obtaining the classification result satisfying differential privacy. The invention can realize high accuracy prediction classification of the high-dimension large-scale data.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a data prediction and classification method and device. Background technique [0002] Classification is an important class of data mining methods. Its purpose is to find out a model that describes and distinguishes data classes or concepts, so that the model can be used to predict the class label of the object. A typical representative of a classification model is a decision tree, which is a tree-shaped classification model. The nodes in the tree represent tests on a certain attribute, and the leaf nodes represent a class. However, both the classification result itself and the classification count value may leak user privacy information. Decision tree classification under traditional privacy protection is mostly realized by data perturbation such as adding random noise or K-anonymity method, or by encrypting original data and intermediate calculation results. However, wh...

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Problems solved by technology

[0005] The present invention provides a data prediction and classification method and device, which can solve the problem that the classification result itself and the classification count value may leak user privacy information

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