安全计算节点聚类系数

K. Areekijseree, Y. Tang, S. Soundarajan
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引用次数: 1

摘要

在执行任何分析任务时,一些信息可能会泄露或分散在不愿意分享其信息的个人之间(例如,个人的朋友数量和他们是谁)。安全多方计算(MPC)允许个人联合执行任何计算,而不泄露每个人的输入。在这里,我们提出了两个新的安全框架,允许节点安全地计算其聚类系数,我们评估了几种建议实例的效率和安全性之间的权衡。我们的研究结果表明,安全计算的成本高度依赖于网络结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing Node Clustering Coefficients Securely
When performing any analysis task, some information may be leaked or scattered among individuals who may not willing to share their information (e.g., number of individual's friends and who they are). Secure multi-party computation (MPC) allows individuals to jointly perform any computation without revealing each individual's input. Here, we present two novel secure frameworks which allow node to securely compute its clustering coefficient, which we evaluate the trade off between efficiency and security of several proposed instantiations. Our results show that the cost for secure computing highly depends on network structure.
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