文章基本信息

标题：Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee
本地全文：下载
作者：Xihui Chen ; Sjouke Mauw ; Yunior Ramírez-Cruz 等
期刊名称：Proceedings on Privacy Enhancing Technologies
电子版ISSN：2299-0984
出版年度：2020
卷号：2020
期号：4
页码：131-152
DOI：10.2478/popets-2020-0066
语种：English
出版社：Sciendo
摘要：We present a novel method for publishing differentially private synthetic attributed graphs. Our method allows,for the first time,to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM,a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter,we introduce differentially private computation methods,which allow us to release communitypreserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments,we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph,as well as degree sequences and clustering coefficients.
关键词：attributed social graphs;generative models; differential privacy;community detection