Plausible Heterogeneous Graph k-Anonymization for Social Networks

作     者：Kaiyang Li Ling Tian Xu Zheng Bei Hui Kaiyang Li;Ling Tian;Xu Zheng;Bei Hui

作者机构：School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 610000China 

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6期

页      面：912-924页

核心收录：

学科分类：1002[医学-临床医学] 07[理学] 1001[医学-基础医学(可授医学、理学学位)] 070104[理学-应用数学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：social network 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 bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give 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.