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.
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 networkswith high probability. Empirical results demonstrate practical applicability with a low 1.07 relative error achievable at strong privacy budget = 0.1 on a Facebook graph, and insignificant performance degradation as the number of network provider parties grows.
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There are discussions about the importance of diversity in literature and in the media and minimizing gaps between minorities and majorities. In order to see if a community is making progress in minimizing these gaps and to measure success, there is an interest in being able to predict the diversity of communities given currently prevailing. There are well-designed data forecasting algorithms in data science using large data sets. However, diversity data has only been collected over the last few decades. This paper adopts algorithms formulated by Grey and ARIMA (Auto-Regressive Integrated Moving Average), using small data to predict the likely diversity of a cohort for a time in the near future. Our results demonstrate there is more confident forecasting for "country of birth", but in terms of predicting linguistic and religious diversity, due to the changeable nature of these factors throughout an individual's life, we would require further data to make any accurate prediction.
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