面向差分隐私保护的随机森林算法

doi:10.19678/j.issn.1000-3428.0053592

摘要/Abstract

摘要： 数据挖掘中的隐私保护问题是目前信息安全领域的研究热点之一。针对隐私保护要求下的分类问题，提出一种面向差分隐私保护的随机森林算法RFDPP-Gini。将随机森林与差分隐私保护相结合，在隐私信息得到保护的同时提高分类的准确率。以CART分类树作为随机森林中的单棵决策树，使用Laplace机制和指数机制添加噪声并选择最佳分裂特征。实验结果表明，RFDPP-Gini算法既能处理离散型特征又能处理连续型特征，在Adult和Mushroom数据集上的分类准确率最高分别达86.335%和100%，且在加入噪声后算法的分类准确率下降幅度极小。

关键词: 隐私保护, 差分隐私, 随机森林, 决策树, CART分类树

Abstract: 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.

Key words: privacy protection, differential privacy, random forest, decision tree, CART classification tree

中图分类号:

TP309

李远航, 陈先来, 刘莉, 安莹, 李忠民. 面向差分隐私保护的随机森林算法[J]. 计算机工程, 2020, 46(1): 93-101.

LI Yuanhang, CHEN Xianlai, LIU Li, AN Ying, LI Zhongmin. Random Forest Algorithm for Differential Privacy Protection[J]. Computer Engineering, 2020, 46(1): 93-101.

https://www.ecice06.com/CN/Y2020/V46/I1/93

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