Link-Privacy Preserving Graph Embedding Data Publication with Adversarial Learning

作     者：Kainan Zhang Zhi Tian Zhipeng Cai Daehee Seo Kainan Zhang;Zhi Tian;Zhipeng Cai;Daehee Seo

作者机构：the Department of Computer ScienceGeorgia State UniversityAtlantaGA 30303USA the Department of Electrical and Computer EngineeringGeorge Mason UniversityFairfaxVA 22030USA the National Center of Excellence in SoftwareSangmyung UniversitySeoul 03016Republic of Korea 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2022年第27卷第2期

页      面：244-256页

核心收录：

学科分类：12[管理学] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 081201[工学-计算机系统结构] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Science Foundation of USA(Nos.1829674 1912753 1704287 and 2011845)。 

主　　题：graph embedding privacy preservation adversarial learning 

摘      要：The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations.Graph embedding methods build an important bridge between social network analysis and data analytics,as social networks naturally generate an unprecedented volume of graph data continuously.Publishing social network data not only brings benefit for public health,disaster response,commercial promotion,and many other applications,but also gives birth to threats that jeopardize each individual’s privacy and security.Unfortunately,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks.To be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding.In this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links,while persevering sufficient non-sensitive information,such as graph topology and node attributes in graph embedding.Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.