【校庆学术活动月】晏挺教授学术报告

发布时间:2020-09-27

报告题目:Directed networks with a differentially private bi-degree sequence

报 告人:晏挺(华中师范大学教授)

报告时间:2020927(周日) 1430-1530

腾讯会议:https://meeting.tencent.com/s/rJ5TSzavXlgN

会议ID124 818 687

报告摘要:

Although many approaches have been developed for releasing network data with a differential privacy guarantee, inference in many network models with differential privacy data is still unknown or has not been properly explored. In this paper, we propose to release the bi-degree sequences of directed networks using the Laplace mechanism and make inference in the $p_0$ model, which is an exponential random graph model with the bi-degree sequence as its exclusively sufficient statistic. We show that the estimator of the parameters without the so-called denoised process is asymptotically consistent and normally distributed. This is in sharp contrast to some known results that valid inference such as the existence and consistency of the estimator requires denoising. Along the way, a new phenomenon is revealed in which an additional variance factor appears in the asymptotic variance of the estimator to account for noise. %when the noise becomes large. An efficient algorithm is proposed for finding the closet point lying in the set of all graphical bi-degree sequences under the global $L_1$ optimization problem. Numerical study demonstrates our theoretical findings

 

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科学技术处

 2020927



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