In order to solve the problem that homogeneous ensemble learning algorithms are more sensitive to noise and difficult to take into account both better predictive performance and effective priva-cy protection,a DPStacking algorithm based on differential privacy is proposed.This algorithm com-bines heterogeneous Stacking algorithms with differential privacy technology to optimize the privacy pro-tection and its predictive performance.However,since both the low-level and high-level models of the Stacking algorithm can be composed of different learners,if a privacy budget allocation scheme is de-signed for a particular learner to provide differential privacy protection,this scheme is often not applica-ble to Stacking algorithms composed of arbitrary base learners and meta-learners.Based on this,a pri-vacy budget allocation scheme based on meta-learners is designed,which allocates different privacy budgets to different components of meta-learners according to the Pearson correlation coefficient and the characteristics of differential privacy parallel combination.Through theoretical and experimental verifi-cation,DPStacking algorithm satisfies ε-differential privacy protection.Compared with differential pri-vacy random forest algorithm(DiffRFs),Adaboost algorithm(DP-AdaBoost),XGBoost algorithm(DPXGB),it can effectively guarantee data privacy while having better predictive performance,and bet-ter solve the problem that single homogeneous ensemble learning algorithm is more sensitive to noise.
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