Can I read Graph Convolutional Networks for Privacy Metrics in Online Social Networks on EtoBox?

Graph Convolutional Networks for Privacy Metrics in Online Social Networks by Li, Xuefeng; Xin, Yang; Zhao, Chensu; Yang, Yixian; Chen, Yuling is a scholarly article available to read on EtoBox.

What is Graph Convolutional Networks for Privacy Metrics in Online Social Networks about?

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 da

Author

Li, Xuefeng; Xin, Yang; Zhao, Chensu; Yang, Yixian; Chen, Yuling

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI AG, 2011- (ISSN 2076-3417)

Published

2020

Language

EN