In order to address the problem that the traditional differential privacy preservation scheme is distributed layer by layer by half of the remaining privacy budget, i.e., equal ratio allocation of privacy budget, and when it is applied to the decision tree, the privacy budget allocated to the top layer is too small, the random noise is too large, and the classification accuracy is affected with the increase of the height of decision tree, a scheme of equal-arrival privacy budget allocation based on decision tree height difference was proposed, which combined differential privacy protection preservation with mainstream decision tree C4.5 classification algorithm. The Laplace mechanism and the exponential mechanism can ensure the security of the decision tree. This scheme utilized the MapReduce framework of the big data Hadoop platform, and the main program performed parameter configuration of MapReduce and outer loop. When executed to each node, the main program passed the statistical task of the dataset attribute to the Mapper class. The Reducer class received the statistical result of the Mapper class. The Laplace mechanism was used to add random noise. The noise-added result was returned to the main program for calculation the information gain rate. The main program used the exponential mechanism to select the best subdivision scheme. The recursion process stopped until the number of samples was 0. The experiment used the car data set of UCI database to test, and compared classification results of two schemes under different privacy budgets. Experiment showed that this scheme can satisfy differential privacy with acceptable classification accuracy reduction, and improve the classification accuracy under the same privacy budget compared to the traditional privacy budget allocation. For the car data set, the algorithm can balance the security and effectiveness of the data set when the privacy budget was 0.7 or 0.8. Therefore, the scheme of equal-arrival privacy budget allocation based on decision tree height difference can improve classification accuracy to a certain extent. It can be practically applied to decision tree classification.
