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Open AccessArticle

Graph Convolutional Networks for Privacy Metrics in Online Social Networks

by Xuefeng Li 1,2, Yang Xin 1,2,*, Chensu Zhao 1,2,3, Yixian Yang 1,2 and Yuling Chen 2
National Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guizhou 550025, China
School of Information and Engineering, Shandong Yingcai University, Jinan 250104, China
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1327; (registering DOI)
Received: 24 December 2019 / Revised: 3 February 2020 / Accepted: 13 February 2020 / Published: 15 February 2020
In recent years, privacy leakage events in large-scale social networks have become increasingly frequent. Traditional methods relying on operators have been unable to effectively curb this problem. Researchers must turn their attention to the privacy protection of users themselves. Privacy metrics are undoubtedly the most effective method. However, social networks have a substantial number of users and a complex network structure and feature set. Previous studies either considered a single aspect or measured multiple aspects separately and then artificially integrated them. The measurement procedures are complex and cannot effectively be integrated. To solve the above problems, we first propose using a deep neural network to measure the privacy status of social network users. Through a graph convolution network, we can easily and efficiently combine the user features and graph structure, determine the hidden relationships between these features, and obtain more accurate privacy scores. Given the restriction of the deep learning framework, which requires a large number of labelled samples, we incorporate a few-shot learning method, which greatly reduces the dependence on labelled data and human intervention. Our method is applicable to online social networks, such as Sina Weibo, Twitter, and Facebook, that can extract profile information, graph structure information of users’ friends, and behavioural characteristics. The experiments show that our model can quickly and accurately obtain privacy scores in a whole network and eliminate traditional tedious numerical calculations and human intervention.
Keywords: Online Social Networks; privacy; graph convolutional networks; metrics; few-shot learning Online Social Networks; privacy; graph convolutional networks; metrics; few-shot learning
MDPI and ACS Style

Li, X.; Xin, Y.; Zhao, C.; Yang, Y.; Chen, Y. Graph Convolutional Networks for Privacy Metrics in Online Social Networks. Appl. Sci. 2020, 10, 1327.

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