FBI: Friendship Learning-Based User Identification in Multiple Social Networks

Fast proliferation of mobile devices significantly promotes the development of mobile social networks. Users tend to interact with friends via multiple social networks. Multiple social networks identification is of great significance in terms of both attack and defense. Current methods either focus...

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Bibliographic Details
Main Authors: Youyang Qu, Shui Yu, Wanlei Zhou, Jianwei Niu
Format: Conference Proceeding
Language:English
Subjects:
Australia
Computer science
Error analysis
Matrix converters
Media
Social networking (online)
Trajectory
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Summary:Fast proliferation of mobile devices significantly promotes the development of mobile social networks. Users tend to interact with friends via multiple social networks. Multiple social networks identification is of great significance in terms of both attack and defense. Current methods either focus on the profile matching or network structure to re-identify a specific user. However, the accuracy are not satisfying with relative high error rate. In this paper, we propose a new Friendship learning-Based Identification (FBI) method to discriminate multiple pseudo identities of a real-world individual. We aim at providing potential attack mechanism to following privacy protection research. Firstly, we develop a new identification method based on friendship matching. Then, we implement a weighted mechanism which takes profile, network structure, and friendship into consideration. Furthermore, machine learning is leverage to further optimize the parameters and improve the accuracy. In addition, extensive experimental results show the superior of the FBI comparing to existing ones.
ISSN:2576-6813
DOI:10.1109/GLOCOM.2018.8647771