Assessing Centrality Without Knowing Connections

• URL: http://arxiv.org/abs/2005.13787v1
• Date: Thu, 28 May 2020 05:51:12 GMT
• Title: Assessing Centrality Without Knowing Connections
• Authors: Leyla Roohi, Benjamin I. P. Rubinstein and Vanessa Teague
• Abstract summary: Motivated by modern communication networks spanning multiple providers, we show for the first time how multiple mutually-distrusting parties can successfully compute node EBC. A theoretical utility analysis upper bounds a primary source of private EBC error---private release of ego networks---with high probability.
• Score: 19.40833089993767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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