论文标题
评估中心性而不知道联系
Assessing Centrality Without Knowing Connections
论文作者
论文摘要
我们考虑了以自我为中心的中心（EBC）衡量的分布式社交网络中节点影响的隐私计算。由现代通信网络跨越多个提供商的动机，我们首次展示了多个相互分配方如何成功计算节点EBC，同时仅揭示有关其内部网络连接的差异性信息。理论上的公用事业分析上限是私人EBC错误的主要来源---私人释放自我网络的可能性很高。经验结果表明，在Facebook图上，在强大隐私预算上可实现的相对错误$ 1.07相对错误，并且随着网络提供商派对的数量的增长，相对错误$ε= 0.1 $。
We consider the privacy-preserving computation of node influence in distributed social networks, as measured by egocentric betweenness centrality (EBC). Motivated by modern communication networks spanning multiple providers, we show for the first time how multiple mutually-distrusting parties can successfully compute node EBC while revealing only differentially-private information about their internal network connections. A theoretical utility analysis upper bounds a primary source of private EBC error---private release of ego networks---with high probability. Empirical results demonstrate practical applicability with a low 1.07 relative error achievable at strong privacy budget $ε=0.1$ on a Facebook graph, and insignificant performance degradation as the number of network provider parties grows.
