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
Source PublicationELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
ISSN1567-4223
2021-11-01
Volume50Pages:17
Corresponding AuthorZheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
AbstractWith 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.
KeywordPrivacy preservation Anonymization Graph neural networks Social network
DOI10.1016/j.elerap.2021.101105
Indexed BySCI
Language英语
Funding ProjectMinistry 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]
Funding OrganizationMinistry 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 Research AreaBusiness & Economics ; Computer Science
WOS SubjectBusiness ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000722136200001
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46534
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorZheng, Xiaolong
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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