Preserving Social Network Privacy Using Edge Vector Perturbation
Preserving Social Network Privacy Using Edge Vector Perturbation
复制标题
使用边缘向量扰动保护社交网络隐私
DOI：
10.1109/iscc-c.2013.103
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Lijun Tian
中科院分区：
文献类型：
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作者：
Lihui Lan;Lijun Tian
通过社交网络应用程序Popularity，研究人员可以通过社交网络分析受益，但它会对社交网络中的个人提出严重的隐私问题。已经提出了一些用于保护个人隐私的技术。然而，现有的方法往往集中在未加权的社会网络匿名的节点和结构信息或加权的社交网络匿名的边缘权重。我们提出了一种边向量扰动方法，以保持加权社交网络的结构特性和边权重。首先，我们构造原始加权社会网络的边向量或边空间。其次，我们计算边缘介数并为边缘向量中的元素分配权重。第三，我们构造释放候选集的加权欧氏距离。我们利用加权社会网络中的边向量和边空间的概念。给定一个社会网络G^s，我们采用两种方法来构造原始边向量E_Vec（G^s），然后从（K_n）中选取一些边向量作为E_Vec（G^s）的发布候选集。为了保证发布数据集的有效性，我们使用向量之间的欧氏距离作为相似性度量。我们在数据集上进行实验，研究出版物的效用和质量。我们的方法可以应用到一个典型的扰动算法，以实现更好地保存其输出的效用。
With the social network application, Popularity, the researchers can benefit through social network analysis, but it raises serious privacy concerns for the individual involved in social network. Some techniques have been proposed for protecting personal privacy. However, the existing methods tend to focus on un-weighted social network for anonymizing nodes and structure information or weighted social networks for anonymizing edge weight. We propose an edge vector perturbation method to preserve structural properties and edge weights for weighted social networks. First, we construct edge vector or edge space of the original weighted social network. Second, we calculate the edge betweenness and assign weights to elements in edge vector. Third, we construct release candidate set by the weighted Euclidean distance. We leverage the notions of edge vector and edge space in weighted social network. Given a social network G^s, we adopt two methods to build original edge vector E_Vec (G^s), and then select from some edge vectors from ψ(K_n)as publication candidate set of E_Vec(G^s). To ensure the effectiveness of released dataset, we use Euclidean distance between the vectors as metrics of the similarity. We execute experiments on datasets to study publication utility and quality. Our method can be applied to a typical perturbation algorithm to achieve better preservation of the utility of its output.