Graph Convolutional Networks for Privacy Metrics in Online Social Networks

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 a...

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Bibliographic Details
Published in:Applied sciences 2020-02, Vol.10 (4), p.1327
Main Authors: Li, Xuefeng, Xin, Yang, Zhao, Chensu, Yang, Yixian, Chen, Yuling
Format: Article
Language:English
Subjects:
Access control
Deep learning
Disclosure
Distance learning
few-shot learning
graph convolutional networks
Information processing
Interdisciplinary subjects
Internet
Literacy
metrics
Neural networks
online social networks
Privacy
Research methodology
Social interactions
Social networks
Social organization
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10041327