User Attribute Completion Attack in Social Networks Based on Node2vec

doi:10.3969/j.issn.1671-1122.2017.12.012

Abstract

Abstract:

In social networks, there is an attack threatening its content security by acquiring user private attributes from attribute inference completion. Traditional user attribute completion methods like unsupervised learning and supervised learning fail to effectively combine homogeneity with structural similarity. This paper presents a user attribute completion attacking method based on implicit expression, which abstracts user attribute completion as a supervised classification problem. The basic idea is to use node2vec algorithm to map the user nodes in social networks into vectors, and then use the clustering method to calculate the community where a node is located, construct the classification model in the community, and use this model to predict the missing attributes of the user. This paper verifies that this algorithm can improve the accuracy of user attribute completion in social networks on a real data set.

Key words: attribute completion, homogeneity, structural similarity, node2vec, content security

CLC Number:

TP309.1

Yang PEI, Xuexin QU, Xiaobo GUO, Dingyang DUAN. User Attribute Completion Attack in Social Networks Based on Node2vec[J]. Netinfo Security, 2017, 17(12): 67-72.

Figures/Tables 6

References 19

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