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 $\epsilon=0.1$ on a Facebook graph, and insignificant
performance degradation as the number of network provider parties grows.

本篇研究采用差分隐私技术，成功实现在不泄露各分布式社交网络内部连接信息的情况下，多个不信任方计算节点的自我中心介数中心度量，实验结果在 Facebook 网络上具有实际可行性。

无需了解连接的核心性评估

Assessing Centrality Without Knowing Connections

We describe a novel protocol for computing the egocentric betweenness
centrality of a node when relevant edge information is spread between two
mutually distrusting parties such as two telecommunications providers. While
each node belongs to one network or the other, its ego network might include
edges unknown to its network provider. We develop a protocol of
differentially-private mechanisms to hide each network's internal edge
structure from the other; and contribute a new two-stage stratified sampler for
exponential improvement to time and space efficiency. Empirical results on
several open graph data sets demonstrate practical relative error rates while
delivering strong privacy guarantees, such as 16% error on a Facebook data set.

提出了一种用于计算节点自我中心介数中心性的新协议，其中相关边缘信息分布在两个彼此不信任的电信提供商之间，并在实际的图形数据集上提供了实用的相对误差率，同时提供了强大的隐私保障。