DiffPRFs：一种面向随机森林的差分隐私保护算法

doi:10.11959/j.issn.1000-436x.2016169

Abstract

Abstract:

A differential privacy algorithm DiffPRFs based on random forests was proposed.Exponential mechanism was used to select split point and split attribute in each decision tree building process,and noise was added according to Laplace mechanism.Differential privacy protection requirement was satisfied through overall process.Compared to existed algorithms,the proposed method does not require pre-discretization of continuous attributes which significantly reduces the performance cost of preprocessing in large multi-dimensional dataset.Classification is achieved conveniently and efficiently while maintains the high accuracy.Experimental results demonstrate the effectiveness and superiority of the algorithm compared to other classification algorithms.

Key words: differential privacy, privacy protection, random forest, data mining

Hai-rong MU,Li-ping DING,Yu-ning SONG,Guo-qing LU. DiffPRFs:random forest under differential privacy[J]. Journal on Communications, 2016, 37(9): 175-182.

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方法	实现机制	具体方法	噪声	框架	数据类型特点
SuLQ-based ID3^[7]	Laplace机制	每次计算属性的信息增益时，使用加入噪声的计数值	高	交互式框架	离散噪声过大，隐私预算消耗多
PINQ-based ID3^[7]	Laplace机制	利用Partition算子将数据集分割成不相交子集，再使用ID3分类	高	交互式框架	离散信息增益计算中避免了预算消耗，但不能降低噪声
DiffPID3^[8]	Laplace 机制、指数机制	利用指数机制来挑选分裂属性	低	交互式框架	离散一次分裂只需消耗一次预算，降低了噪声
DiffP-C4.5^[8]	Laplace 机制、指数机制	将指数机制扩展到了连续属性	低	交互式框架	离散连续每次迭代须先用指数机制对连续属性选择分裂点
DiffGen^[9]	Laplace 机制、指数机制	先将数据集泛化，之后细分迭代循环。结合指数机制与信息增益来确定分裂属性	低	非交互式框架	离散连续属性维度大时指数机制效率低，且可能耗尽隐私预算
DT-Diff^[10]	Laplace 机制、指数机制	完全泛化再逐步细分，将连续属性细分方案以相应权重和离散属性细分方案一起调用指数机制选择	低	非交互式框架	离散连续减少调用指数机制次数，提高隐私预算的利用率
DiffPRF^[11]	Laplace 机制、指数机制	利用随机森林建立 ID3 决策树进行分类	低	交互式框架	离散利用随机森林解决维度问题，需要预先将连续属性离散化

DiffPRFs:random forest under differential privacy

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