Visible to the public MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks

TitleMobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks
Publication TypeJournal Article
Year of Publication2014
AuthorsFei Hao, Geyong Min, Man Lin, Changqing Luo, Yang, L.T.
JournalParallel and Distributed Systems, IEEE Transactions on
Date PublishedNov
KeywordsComputational modeling, Context, Context modeling, distributed public virtual social spaces, fuzzy inference, fuzzy linguistic technique, fuzzy reasoning, fuzzy set theory, fuzzy trust inference mechanism, graph theory, linguistic terms, MobiFuzzyTrust inference mechanism, Mobile communication, mobile computing, mobile context, mobile context aware trust model, mobile devices, Mobile handsets, mobile social networks, mobile users, MSN, nonsemantical trust representation, Pragmatics, real-world mobile dataset, security of data, social links, Social network services, social networking (online), Trust, trust graph, trust models, trust value evaluation, Trusted Computing

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.

Citation Key6684155