Table of Content

Open AccessOpen Access


Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

Yabin Xu1, 2, *, Xuyang Meng1, Yangyang Li3, Xiaowei Xu4, *

1 School of Computer, Beijing Information Science & Technology University, Beijing, 100101, China.
2 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing, 100101, China.
3 Innovation Center, China Academy of Electronics and Information Technology, Beijing, 100041, China.
4 Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, USA.

* Corresponding Authors: Yabin Xu. Email: ;
  Xiaowei Xu. Email: .

Computers, Materials & Continua 2020, 62(1), 137-155.


In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users, this paper takes microblog as the research object to study the detection of privacy disclosure in social networks. First, we perform fast privacy leak detection on the currently published text based on the fastText model. In the case that the text to be published contains certain private information, we fully consider the aggregation effect of the private information leaked by different channels, and establish a convolution neural network model based on multi-dimensional features (MF-CNN) to detect privacy disclosure comprehensively and accurately. The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.


Cite This Article

Y. Xu, X. Meng, Y. Li and X. Xu, "Research on privacy disclosure detection method in social networks based on multi-dimensional deep learning," Computers, Materials & Continua, vol. 62, no.1, pp. 137–155, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1429


  • 1041


  • 0


Share Link