A Location Spoofing Detection Method for Social Networks (Short Paper)

2019 ◽  
pp. 138-150 ◽  
Author(s):  
Chaoping Ding ◽  
Ting Wu ◽  
Tong Qiao ◽  
Ning Zheng ◽  
Ming Xu ◽  
...  
Keyword(s):  
Social Networks ◽  
Detection Method ◽  
Short Paper ◽  
Spoofing Detection ◽  
Location Spoofing

scholarly journals Exploiting Proximity-Based Mobile Apps for Large-Scale Location Privacy Probing

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Shuang Zhao ◽  
Xiapu Luo ◽  
Xiaobo Ma ◽  
Bo Bai ◽  
Yankang Zhao ◽  
...  
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Real World ◽  
Emission Reduction ◽  
Large Scale ◽  
Location Privacy ◽  
Mobile Apps ◽  
Physical World ◽  
Typical Type ◽  
Location Spoofing

Proximity-based apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, proximity-based nearby-stranger (NS) apps that encourage people to make friends with nearby strangers have gained popularity recently. As another typical type of proximity-based apps, some ridesharing (RS) apps allowing drivers to search nearby passengers and get their ridesharing requests also become popular due to their contribution to economy and emission reduction. In this paper, we concentrate on the location privacy of proximity-based mobile apps. By analyzing the communication mechanism, we find that many apps of this type are vulnerable to large-scale location spoofing attack (LLSA). We accordingly propose three approaches to performing LLSA. To evaluate the threat of LLSA posed to proximity-based mobile apps, we perform real-world case studies against an NS app named Weibo and an RS app called Didi. The results show that our approaches can effectively and automatically collect a huge volume of users’ locations or travel records, thereby demonstrating the severity of LLSA. We apply the LLSA approaches against nine popular proximity-based apps with millions of installations to evaluate the defense strength. We finally suggest possible countermeasures for the proposed attacks.

Community detection in social networks based on information propagation and user engagement

2021 ◽  
pp. 2150119
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Method ◽  
User Engagement ◽  
Connection Strength ◽  
Information Propagation ◽  
Edge Strength ◽  
Strength Based ◽  
Network Propagation ◽  
Overlapping Community

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.

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