 Trust evaluation based on evidence theory in online social networksJian Wang, Kuoyuan Qiao and Zhiyong Zhang International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 10, 1550147718794629 Abstract: 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. Keywords: Trust evaluation; evidence theory; online social networks; information flow prediction; decision making (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:10:p:1550147718794629 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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