中南民族大学学报（自然科学版）2025，Vol.44Issue(5)：685-694,10.DOI:10.20056/j.cnki.ZNMDZK.20250514
差分隐私条件下有向加权网络的渐近理论
Asymptotic theory of directed weighted networks under differential privacy
摘要
Abstract
This study investigates the asymptotic distribution of linear combinations of parameter estimators in directed weighted network model under differential privacy constraints.It is demonstrated that as the number of network vertices increases,the linear combination of model parameter estimators converge to an asymptotic normal distribution,revealing its asymptotic theory under differential privacy protection.Furthermore,numerical simulations validate the theoretical effectiveness,providing novel theoretical tools and analytical methodologies for statistical inference of network data under differential privacy protection.关键词
有向网络/加权模型/参数估计/差分隐私/渐近正态性Key words
directed network/weighted model/parameter estimator/differential privacy/asymptotic normality分类
数理科学引用本文复制引用
秦兆伦,罗敬..差分隐私条件下有向加权网络的渐近理论[J].中南民族大学学报（自然科学版）,2025,44(5):685-694,10.基金项目
教育部人文社会科学研究资助项目(24YJC910006) （24YJC910006）
中央高校基本科研业务费专项资金资助项目(CZQ24018) （CZQ24018）
