Trust evaluation based on evidence theory in online social networks
Trust evaluation based on evidence theory in online social networks
复制标题
在线社交网络中基于证据理论的信任评估
DOI：
10.1177/1550147718794629
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发表时间：
2018-10
影响因子：
2.3
通讯作者：
Zhang Zhiyong
中科院分区：
文献类型：
--
作者：
Wang Jian;Qiao Kuoyuan;Zhang Zhiyong
信任是在线社交网络隐私保护领域访问控制的重要准则。现有的方法忽略了信任的主观性和个体性，为所有用户建立了一个固定的模型。事实上，不同的用户在做出信任决策时可能会考虑不同的信任特征。此外，在现有的方案中，只有用户的静态特征映射到信任值，没有隐私泄漏的风险。在本文中，每个用户在做出信任决策时所关心的功能都是通过机器学习挖掘出来的，以满足用户的意愿。通过信息流预测来估计被评估用户的隐私泄露风险。然后将用户意愿和隐私泄露风险映射为信任证据，并通过改进的证据理论证据合成规则进行合成。最后，在Epperly数据集上比较了几种典型方法和本文提出的方案的性能。通过比较信任度评估结果的F-Score和Mean Error，验证了该方案的优越性。
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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