在线社交网络中信任评估的混合算法

Nina Fatehi, H. Shahhoseini
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引用次数: 1

摘要

由于在线社交网络(sns)提供的各种服务,其普及速度的加快是不可避免的。这就是为什么在osn中,安全作为一种保护用户的私人数据不被未经授权的人滥用的方式发挥着至关重要的作用。信任评估是自osn出现以来一直使用的安全方法。基于图的方法是最流行的信任评估方法之一。然而,基于图的模型需要在查找可信路径的搜索过程中使用限制。这将导致信任准确性的降低。在本研究中,提出了一种基于学习的模型,该模型不受任何限制,能够找到任何目标用户的可靠用户。实验结果表明,与基于图的方法相比,信任精度提高了12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Algorithm for Evaluating Trust in Online Social Networks
The acceleration of extending popularity of Online Social Networks (OSNs) thanks to various services with which they provide people, is inevitable. This is why in OSNs security as a way to protect private data of users to be abused by unauthoritative people has a vital role to play. Trust evaluation is the security approach that has been utilized since the advent of OSNs. Graph-based approaches are among the most popular methods for trust evaluation. However, graph-based models need to employ limitations in the search process of finding trusted paths. This contributes to a reduction in trust accuracy. In this investigation, a learning-based model which with no limitation is able to find reliable users of any target user, is proposed. Experimental results depict 12% improvement in trust accuracy compares to models based on the graph-based approach.
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