基于节点分割的权重序列隐私保护研究

针对加权社会网络的数据发布和权重序列攻击,提出采用基于节点分割的权重序列匿名方法实现网络结构、边权重以及权重序列的隐私保护。此方法在图的边空间概念的基础上,采用节点聚类分割思想构建加权社会网络的向量集模型,利用边的介数中心性重新分配权重,通过组内直径距离和节点的相对距离调整权重序列,利用加权欧式距离构建候选向量集,从候选向量集中选取向量实现社会网络的数据发布。此方法在保证隐私安全的前提下,保证了社会网络分析需要的结构特征、发布数据的有效性以及有效地抵御了权重序列攻击。

Research on weight protection of weighted sequences based on node segmentation

Aiming at the data release and weight sequence attacks of weighted social networks, this paper proposes a weighting anonymous method based on node segmentation to realize the privacy protection of network structure, side weight and weight sequence. Based on the concept of edge space of the graph, this paper constructs the vector set model of the weighted social network by using the idea of node clustering segmentation, reallocates the weight by using the center of the interval, adjusts the weight by the distance between the group diameter and the relative distance of the node Sequence, the weighted Euclidean distance is used to construct the candidate vector set, and the vector is selected from the candidate vector set to realize the social network. This method guarantees the structural characteristics needed by the social network analysis, the validity of the published data and the effective protection of the weight sequence attacks under the premise of ensuring the privacy and security.