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A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks

机译:在线社交网络的基于信任的隐私保护朋友推荐方案

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摘要

Online social networks (OSNs), which attract thousands of million people to use everyday, greatly extend OSN users’ social circles by friend recommendations. OSN users’ existing social relationship can be characterized as 1-hop trust relationship, and further establish a multi-hop trust chain during the recommendation process. As the same as what people usually experience in the daily life, the social relationship in cyberspaces are potentially formed by OSN users’ shared attributes, e.g., colleagues, family members, or classmates, which indicates the attribute-based recommendation process would lead to more fine-grained social relationships between strangers. Unfortunately, privacy concerns raised in the recommendation process impede the expansion of OSN users’ friend circle. Some OSN users refuse to disclose their identities and their friends’ information to the public domain. In this paper, we propose a trust-based privacy-preserving friend recommendation scheme for OSNs, where OSN users apply their attributes to find matched friends, and establish social relationships with strangers via a multi-hop trust chain. Based on trace-driven experimental results and security analysis, we have shown the feasibility and privacy preservation of our proposed scheme.
机译:在线社交网络(OSN)每天吸引成千上万人使用,它通过朋友的推荐大大扩展了OSN用户的社交圈。 OSN用户现有的社交关系可以被描述为一跳信任关系,并在推荐过程中进一步建立多跳信任链。与人们通常在日常生活中所经历的相同,网络空间中的社会关系可能由OSN用户的共享属性(例如同事,家人或同学)形成,这表明基于属性的推荐过程将导致更多陌生人之间的社交关系细密。不幸的是,在推荐过程中引发的隐私问题阻碍了OSN用户的朋友圈的扩大。一些OSN用户拒绝向公共领域公开其身份和朋友的信息。在本文中,我们为OSN提出了一种基于信任的隐私保护朋友推荐方案,其中OSN用户使用其属性来找到匹配的朋友,并通过多跳信任链与陌生人建立社交关系。基于跟踪驱动的实验结果和安全性分析,我们已经证明了该方案的可行性和隐私保护。

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