T-OSN: A Trust Evaluation Model in Online Social Networks

2011 ◽  
Author(s):  
Ming Li ◽  
Alessio Bonti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Evaluation Model ◽  
Trust Evaluation

Trust Evaluation in Online Social Networks Using Generalized Network Flow

2016 ◽  
Vol 65 (3) ◽  
pp. 952-963 ◽  
Author(s):  
Wenjun Jiang ◽  
Jie Wu ◽  
Feng Li ◽  
Guojun Wang ◽  
Huanyang Zheng
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Network Flow ◽  
Trust Evaluation ◽  
Generalized Network Flow

Understanding Graph-Based Trust Evaluation in Online Social Networks

2016 ◽  
Vol 49 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Wenjun Jiang ◽  
Guojun Wang ◽  
Md Zakirul Alam Bhuiyan ◽  
Jie Wu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Trust Evaluation

scholarly journals Trust evaluation based on evidence theory in online social networks

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771879462 ◽  
Author(s):  
Jian Wang ◽  
Kuoyuan Qiao ◽  
Zhiyong Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Preservation ◽  
Evidence Theory ◽  
Trust Evaluation ◽  
Combination Rule ◽  
Privacy Leakage ◽  
Evidence Combination ◽  
Fixed Model ◽  
The Mean

Trust is an important criterion for access control in the field of online social networks privacy preservation. In the present methods, the subjectivity and individualization of the trust is ignored and a fixed model is built for all the users. In fact, different users probably take different trust features into their considerations when making trust decisions. Besides, in the present schemes, only users’ static features are mapped into trust values, without the risk of privacy leakage. In this article, the features that each user cares about when making trust decisions are mined by machine learning to be User-Will. The privacy leakage risk of the evaluated user is estimated through information flow predicting. Then the User-Will and the privacy leakage risk are all mapped into trust evidence to be combined by an improved evidence combination rule of the evidence theory. In the end, several typical methods and the proposed scheme are implemented to compare the performance on dataset Epinions. Our scheme is verified to be more advanced than the others by comparing the F-Score and the Mean Error of the trust evaluation results.

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