Differentially-Private Two-Party Egocentric Betweenness Centrality
This addresses privacy concerns for entities like telecommunications providers sharing sensitive network data, though it is an incremental improvement in secure multi-party computation.
The paper tackles the problem of computing egocentric betweenness centrality when edge information is split between two distrusting parties, developing a differentially-private protocol to protect each network's internal structure while achieving practical error rates like 16% on a Facebook dataset.
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.