Crowds replace experts: Building better location-based services using mobile social network interactions

2012 ◽  
Author(s):  
Pravin Shankar ◽  
Yun-Wu Huang ◽  
Paul Castro ◽  
Badri Nath ◽  
Liviu Iftode
Keyword(s):  
Social Network ◽  
Location Based Services ◽  
Mobile Social Network

scholarly journals RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jinquan Zhang ◽  
Yanfeng Yuan ◽  
Xiao Wang ◽  
Lina Ni ◽  
Jiguo Yu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Mobile Social Network ◽  
Location Service ◽  
Simulation Results ◽  
User Location

Applying the proliferated location-based services (LBSs) to social networks has spawned mobile social network (MSN) services that allow users to discover potential friends around them. While enjoying the convenience of MSN services, the mobile users also are confronted with the risk of location disclosure, which is a severe privacy preserving concern. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a repartitioning anonymous region for location privacy preserving (RPAR) scheme based on the central anonymous location which minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. Furthermore, our scheme enables the users to get more accurate query results, thus improving the quality of the location service. Simulation results show that our proposed scheme can effectively reduce the area of anonymous regions and minimize the traffic.

scholarly journals PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

scholarly journals Cooperation Dynamics on Mobile Crowd Networks of Device-to-Device Communications

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yong Deng ◽  
Guiyi Wei ◽  
Mande Xie ◽  
Jun Shao
Keyword(s):  
Social Network ◽  
Resource Sharing ◽  
Evolutionary Game ◽  
Influence Factors ◽  
Smart Devices ◽  
Mobile Social Network ◽  
Crowd Density ◽  
Cooperation Rate ◽  
Cooperative Behaviors ◽  
Mobile Crowd

The explosive use of smart devices enabled the emergence of collective resource sharing among mobile individuals. Mobile users need to cooperate with each other to improve the whole network’s quality of service. By modeling the cooperative behaviors in a mobile crowd into an evolutionary Prisoner’s dilemma game, we investigate the relationships between cooperation rate and some main influence factors, including crowd density, communication range, temptation to defect, and mobility attributes. Using evolutionary game theory, our analysis on the cooperative behaviors of mobile takes a deep insight into the cooperation promotion in a dynamical network with selfish autonomous users. The experiment results show that mobile user’s features, including speed, moving probability, and reaction radius, have an obvious influence on the formation of a cooperative mobile social network. We also found some optimal status when the crowd’s cooperation rate reaches the best. These findings are important if we want to establish a mobile social network with a good performance.

scholarly journals A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

2018 ◽  
Vol 129 ◽  
pp. 368-371 ◽  
Author(s):  
Lina Ni ◽  
Yanfeng Yuan ◽  
Xiao Wang ◽  
Mengmeng Zhang ◽  
Jinquan Zhang
Keyword(s):  
Social Network ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Social Network
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