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一种基于动态分类树构造的集值型数据差分隐私保护方法
引用本文:郑 剑,黄奚芳,刘 聪.一种基于动态分类树构造的集值型数据差分隐私保护方法[J].计算机应用研究,2015,32(8).
作者姓名:郑 剑  黄奚芳  刘 聪
作者单位:江西理工大学 信息工程学院,江西理工大学 信息工程学院,江西理工大学 信息工程学院
基金项目:江西省教育厅科学技术研究项目;江西理工大学科研基金重点课题
摘    要:基于分类树划分的差分隐私方法能有效地对集值型数据的发布进行保护,但在构造分类树时该方法没有充分利用集值型数据集自身的特征。通过对添加噪音量的影响因素分析,提出了一种基于数据集特征的集值型数据发布方法,该方法首先对数据集进行分析,然后根据数据集中记录的种类数占总输出域的比例以及只出现一次的记录种类数占总输出域比例,动态构造分类树。实验结果表明:当数据集满足 且 时,通过有效利用集值型数据集的特征,构造较优的分类树,可以添加少于10%的噪音。

关 键 词:分类树  差分隐私保护  集值型数据  数据集特征
收稿时间:2014/6/17 0:00:00
修稿时间:6/6/2015 12:00:00 AM

A constructing Taxonomy Tree based Dynamic Method for Differential Privacy Preserving Set-valued Data
Zheng Jian,Huang Xi-fang and Liu cong.A constructing Taxonomy Tree based Dynamic Method for Differential Privacy Preserving Set-valued Data[J].Application Research of Computers,2015,32(8).
Authors:Zheng Jian  Huang Xi-fang and Liu cong
Affiliation:school of information , JiangXi university of science and technology,,school of information ,JiangXi university of science and technology
Abstract:Taxonomy tree partitioning based method for differential privacy could protect the effective releasing of set-valued data. However, taxonomy tree does not take the characteristics of set-valued datasets into consideration of tree construction. By analyzing the influence factors of added noise, this paper proposes a novel method that releases set-valued data based on the characteristics of datasets. This method firstly analyzes the datasets, and then dynamically forms taxonomy tree structure according to the types of records in the dataset and the proportion between the total output of a single record field and the total number of species appeared in proportional output fields. The experimental results show that the proposed method could effectively utilize the characteristics of set-valued datasets, when the datasets conditions satisfies and , constructing superior taxonomy tree and reducing noise to less than 10%.
Keywords:taxonomy tree  differential privacy  set-valued data  datasets characteristics
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