Adversarial Privacy-Preserving Graph Embedding Against Inference Attack

被引:43
作者
Li, Kaiyang [1 ,2 ]
Luo, Guangchun [1 ,2 ]
Ye, Yang [3 ]
Li, Wei [3 ]
Ji, Shihao [3 ]
Cai, Zhipeng [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 600731, Peoples R China
[2] Univ Elect Sci & Technol China, Trusted Cloud Comp & Big Data Key Lab Sichuan Pro, Chengdu 600731, Peoples R China
[3] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30302 USA
基金
中国国家自然科学基金;
关键词
Privacy; Internet of Things; Social networking (online); Topology; Inference algorithms; Differential privacy; Network topology; Adversarial learning; data privacy; graph embedding; inference attack; INTERNET;
D O I
10.1109/JIOT.2020.3036583
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media, etc., has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph-structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, the existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this article, we propose adversarial privacy graph embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph data sets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/KaiyangLi1992/Privacy-Preserving-Social-Network-Embedding.
引用
收藏
页码:6904 / 6915
页数:12
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