Privacy preserving based on vector similarity for weighted social networks

被引：0
作者：
Lan, Li-Hui [1 ,2 ]
Ju, Shi-Guang [1 ]
机构：
[1] School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu
[2] School of Information Engineering, Shenyang University, Shenyang, 110000, Liaoning
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 08期
关键词：
Edge weight; Privacy preserving; Social networks; Vector set model; Weighted Euclidean distance;
D O I：
10.3969/j.issn.0372-2112.2015.08.015
中图分类号：
学科分类号：
摘要：
Aiming at the publication of weighted social networks, a random perturbation method based on vector similarity is proposed. It can protect network structures and edge weights in multiple release scenarios. It constructs vector set models by segmentation based on vertex cluster using edge space theory. It adopts weighted Euclidean distance as similarity metrics to construct the released candidate sets according to the threshold. It randomly selects vectors from candidate sets to construct the published weighted social networks. The proposed method can resist multiple vertex recognition attacks, force attackers to re-identify in a large result set that the existential probabilities of the vectors are same, and increase the uncertainty of recognition. The experimental results demonstrate that it can preserve individuals' privacy security, meanwhile it can protect some structure characteristics for networks analysis and improve data utility. ©, 2015, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：1568 / 1574
页数：6
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