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A novel trust-based access control for social networks using fuzzy systems

机译:基于模糊系统的新型基于信任的社交网络访问控制

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摘要

Social networks are constantly expanding and attracting more and more users. The users of these networks share plenty of information with their friends, many of which are confidential and private. In this regard, maintaining the security and privacy of users is a major requirement in social networks. Although the traditional access control can help users keep their privacy by applying initial access levels, they are not effective for social networks, considering the dynamic nature of them. Therefore, in this paper, a novel trust-based access control approach has been presented for social network using fuzzy system. The proposed method, SNFTrust, is a combination of trust-based access control and fuzzy inference system which consists of three modules: the user module, the fuzzy trust module, and the access control module. In user module, the user request is analyzed to identify the type of relationships, and the property matrix is created based on user's activity in social network. In fuzzy trust module, two fuzzy systems are combined to calculate the trust score and to specify the access right. Finally, the access control module enables access to the user account. The proposed approach was implemented using the dataset of a real microblog that attracted over 540 million users on the time we accessed the dataset. The results of experiments indicate that the amount of accuracy is 0.96 and the proposed method has the required flexibility, scalability and accuracy, which can be suitable to apply in various social networks.
机译:社交网络不断扩展并吸引越来越多的用户。这些网络的用户与他们的朋友共享大量信息,其中许多都是机密和私人的。在这方面,维护用户的安全性和隐私是社交网络中的主要要求。尽管传统的访问控制可以通过应用初始访问级别来帮助用户保持隐私,但是考虑到它们的动态性质,它们对于社交网络并不有效。因此,本文提出了一种新的基于模糊系统的基于信任的社交网络访问控制方法。所提出的方法SNFTrust是基于信任的访问控制和模糊推理系统的组合,该系统由三个模块组成:用户模块,模糊信任模块和访问控制模块。在用户模块中,分析用户请求以识别关系的类型,并基于用户在社交网络中的活动创建属性矩阵。在模糊信任模块中,将两个模糊系统组合起来以计算信任分数并指定访问权限。最后,访问控制模块允许访问用户帐户。所提出的方法是使用真实微博的数据集实施的,该数据集在我们访问数据集时吸引了5.4亿用户。实验结果表明,该方法的准确度为0.96,具有所需的灵活性,可扩展性和准确性,可适用于各种社交网络。

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