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Trust evaluation based on evidence theory in online social networks

Author

Listed:
  • Jian Wang
  • Kuoyuan Qiao
  • Zhiyong Zhang

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.

Suggested Citation

  • Jian Wang & Kuoyuan Qiao & Zhiyong Zhang, 2018. "Trust evaluation based on evidence theory in online social networks," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477187, October.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:10:p:1550147718794629
    DOI: 10.1177/1550147718794629
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    Cited by:

    1. Liguo Fei & Jun Xia & Yuqiang Feng & Luning Liu, 2019. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.

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