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首页> 外文期刊>Future generation computer systems >ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks
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ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks

机译：ARMOR：在线社交网络中用于分散好友推荐的基于信任的隐私保护框架

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摘要

AbstractFriend recommendation in online social networks (OSNs) has recently experienced rapid development and received much research attention. Existing recommender systems on the basis of the big social data mostly employ centralized framework, which would cause lots of problems, such as single point failure, communication bottleneck and so on. Some other studies focus on decentralized framework for recommendation, however, most of them concentrate on the improvement of recommendation quality, while underestimating privacy issues, e.g. OSN users’ privacy concerns regarding their social relationships, social attributes, and recommendation profiles. In this paper, we propose a novel decentralized framework, namely ARMOR, which utilizes OSN users’ social attributes and trust relationships to achieve the friend recommendation in a privacy-preserving manner. In ARMOR, we adopt a light-weight privacy-preserving protocol to aggregate the utilities of multi-hop trust chains and compute the recommender results securely. We also analyze the efficiency of ARMOR in theory and prove that OSN users’ privacy can be preserved. Finally, we conduct an experiment to evaluate ARMOR over a real-world dataset and empirical results demonstrate that our ARMOR can effectively and efficiently recommend friends in a privacy-preserving way.Highlights•We propose a privacy-preserving framework to achieve the friend recommendation in OSN.•We adopt a secure protocol to compute the utilities and the recommender results.•We also theoretically analyze the efficiency and effectiveness of our framework.•Evaluation results demonstrate that ARMOR is effective and efficient in recommendation.
机译： 摘要 在线社交网络（OSN）中的朋友推荐最近经历了快速发展，并受到了很多研究关注。现有的基于大社交数据的推荐系统大多采用集中化框架，这会引起很多问题，例如单点故障，通信瓶颈等。其他一些研究则集中在分散的推荐框架上，但是，大多数研究都集中在提高推荐质量上，同时低估了隐私问题，例如OSN用户关于其社交关系，社交属性和推荐个人资料的隐私问题。在本文中，我们提出了一个新颖的分散框架，即ARMOR，该框架利用OSN用户的社交属性和信任关系以保护隐私的方式获得好友推荐。在ARMOR中，我们采用了一种轻量级的隐私保护协议来聚合多跳信任链的实用程序，并安全地计算推荐结果。我们还从理论上分析ARMOR的效率，并证明OSN用户的隐私可以得到保护。最后，我们进行了一个实验，以评估真实数据集上的ARMOR，经验结果表明，我们的ARMOR可以以隐私保护的方式有效，高效地推荐朋友。 突出显示 • 我们提出了一个隐私保护框架，以实现OSN中的好友推荐。 • 我们采用安全协议来计算实用程序以及推荐结果。 • 我们还从理论上分析了我们框架的效率和有效性。 • 评估结果表明ARMOR在推荐方面是有效的。 < / ce：list>

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  • 来源
    《Future generation computer systems》 |2018年第1期|82-94|共13页
  • 作者

    Xindi Ma; Jianfeng Ma; Hui Li; Qi Jiang; Sheng Gao;

  • 作者单位

    School of Cyber Engineering, Xidian University;

    School of Cyber Engineering, Xidian University;

    School of Cyber Engineering, Xidian University;

    School of Cyber Engineering, Xidian University,School of Computer & Software, Nanjing University of Information Science & Technology;

    School of Information, Central University of Finance and Economics;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Friend recommendation; Online social network; Privacy preservation; Trust;

    机译：朋友推荐;在线社交网络;隐私保护;信任;

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