Random Forest Algorithm for Differential Privacy Protection
摘要
Privacy protection in data mining is one of the research hotspots in the field of information security.To address the classification problem under privacy protection requirements,this paper proposes a random forest algorithm RFDPP-Gini for differential privacy protection.The random forest and differential privacy protection are combined to improve the classification accuracy while guaranteeing the protection of private information.The CART classification tree is taken as a single decision tree in the random forest.The Laplace mechanism and the exponential mechanism are used to add noise and select the optimal splitting feature.Experimental results show that the RFDPP-Gini algorithm can deal with both discrete and continuous features.The classification accuracy on Adult and Mushroom datasets can reach up to 86.335% and 100% respectively,and the magnitude of classification accuracy decline is very slight after noise is added.