Knowledge Commons of Institute of Automation,CAS
epsilon-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks | |
Tian, Hu1,2; Zheng, Xiaolong1,2; Zhang, Xingwei1,2; Zeng, Daniel Dajun1,2 | |
发表期刊 | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS |
ISSN | 1567-4223 |
2021-11-01 | |
卷号 | 50页码:17 |
摘要 | With the explosive growth of social networks, privacy preservation as a social good has been one common concern. Graph neural networks (GNNs) have been utilized by social network service providers to improve business service. However, traditional anonymization techniques of social networks cannot satisfy the desired privacy preservation of node attribute and graph structure and introduce information disturbance from the anonymization, leading to the performance degradation of GNNs in social network analysis. To protect sensitive user data and persist GNNs' performance in social network analysis, we propose a two-stage privacy-preserving method of graph neural networks in the social network domain. During the first stage, we design a novel e-k anonymization method that can achieve e-local differential privacy (e-LDP) and k-degree anonymity by incorporating the classical LDP and k-degree anonymization (k-DA) while retaining as much network community information as possible. At the second stage, we develop an adversarial training mechanism for GNNs to resist the disturbance from e-k anonymization and retain as much task performance as possible on anonymous social network data. Comprehensive experiments on several real-world social network datasets demonstrate the effectiveness of the proposed method for privacy-preserving node classification, link prediction, and graph clustering in social networks. The proposed method represents an interesting and important combination of classical anonymous technologies and recent GNNs and can preserve user privacy while providing business service. |
关键词 | Privacy preservation Anonymization Graph neural networks Social network |
DOI | 10.1016/j.elerap.2021.101105 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Ministry of Health of China, China[2017ZX10303401-002] ; Ministry of Science and Technology of China, China[2020AAA0108401] ; Natural Science Foundation of China, China[71472175] ; Natural Science Foundation of China, China[71602184] ; Natural Science Foundation of China, China[71621002] ; National Key Research and Development Program of China, China[2016QY02D0305] |
项目资助者 | Ministry of Health of China, China ; Ministry of Science and Technology of China, China ; Natural Science Foundation of China, China ; National Key Research and Development Program of China, China |
WOS研究方向 | Business & Economics ; Computer Science |
WOS类目 | Business ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications |
WOS记录号 | WOS:000722136200001 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 社会计算 |
国重实验室规划方向分类 | 社会系统建模与计算 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46534 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Zheng, Xiaolong |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Tian, Hu,Zheng, Xiaolong,Zhang, Xingwei,et al. epsilon-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks[J]. ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS,2021,50:17. |
APA | Tian, Hu,Zheng, Xiaolong,Zhang, Xingwei,&Zeng, Daniel Dajun.(2021).epsilon-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks.ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS,50,17. |
MLA | Tian, Hu,et al."epsilon-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks".ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 50(2021):17. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
tian-ecra-2021pdf.pd(2484KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论