ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (12): 2794-2809.doi: 10.7544/issn1000-1239.2018.20170756

• 软件技术 • 上一篇    



  1. 1(河南财经政法大学计算机与信息工程学院 郑州 450002);2(河南财经政法大学网络信息安全研究所 郑州 450046);3(中国人民大学信息学院 北京 100872) (
  • 出版日期: 2018-12-01
  • 基金资助: 

Private High-Dimensional Data Publication with Junction Tree

Zhang Xiaojian1, Chen Li2, Jin Kaizhong1, Meng Xiaofeng3   

  1. 1(College of Computer & Information Engineering, He’nan University of Economics and Law, Zhengzhou 450002);2(Institute of Network Information Security, He’nan University of Economics and Law, Zhengzhou 450046);3(School of Information, Renmin University of China, Beijing 100872)
  • Online: 2018-12-01

摘要: 基于差分隐私的数据发布已得到研究者的广泛关注.然而,现有的发布方法却不能有效地处理高维数据,其原因在于维度灾难和值域多样会引入极大的噪音值,进而使得发布结果的可用性比较低.基于此,提出一种基于联合树的隐私高维数据发布方法PrivHD(differentially private high dimensional data release),该方法通过指数机制构造Markov网,引入满足差分隐私的高通滤波技术缩减指数机制搜索空间.结合充分三角化操作和顶点消除操作对Markov网分割来获得完全团图,采用最大生成树方法生成满足差分隐私的联合树.利用联合树中各个团后置处理之后的联合分布表合成最终的高维数据.基于真实的高维数据集比较PrivHD算法与PrivBayes(private Bayesian network),JTree(junction tree)算法的精度,实验结果表明:PrivHD算法的k-way查询和SVM(support vector machine)分类精度优于同类算法.

关键词: 高维数据, 差分隐私, Markov网, 联合树, 边缘分布

Abstract: The problem of differentially private data publishing has attracted considerable research attention in recent years. The current existing solutions, however, cannot effectively handle the release of high-dimensional data. That is because these methods suffer from curse of dimensionality and various domain sizes, which will lead to the lower utility of publication. To address the problems, this paper presents PrivHD (differentially private high dimensional data release) with junction tree, a differentially private method for publishing high-dimensional data. PrivHD firstly generates a Markov network with exponential mechanism, which employs the high-pass filter technique to reduce the candidate space in the sampling process. After that, based on the network, PrivHD obtains a complete cluster graph in terms of full triangulation and node elimination, and then relies on the cluster graph and maximum spanning tree method to construct a differentially private junction tree. Finally, PrivHD uses the post-processing technique to boost the noisy counts of marginal tables in each cluster in junction tree, and based on the boosted result, PrivHD produces the high-dimensional synthetic dataset. PrivHD is compared with the existing approaches such as PrivBayes, JTree on the different real datasets. The experimental results show that PrivHD is better than its competitors on k-way query and SVM classification.

Key words: high-dimensional data, differential privacy, Markov network, junction tree, marginal distribution