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Federated Tensor Mining for Secure Industrial Internet of Things
Kong LH(孔令和)1; Liu, Xiao-Yang2; Sheng H(盛浩)3; Zeng P(曾鹏)4; Chen, Guihai1
Department工业控制网络与系统研究室
Corresponding AuthorSheng, Hao(shenghao@buaa.edu.cn)
Source PublicationIEEE Transactions on Industrial Informatics
ISSN1551-3203
2020
Volume16Issue:3Pages:2144-2153
Indexed BySCI ; EI
EI Accession number20200508103270
WOS IDWOS:000510903200068
Contribution Rank4
Funding OrganizationNational Key R&D Program of China under Grant 2018YFB1004703 ; National Natural Science Foundation of China under Grant 61861166002 ; Science and Technology Development Fund of Macau SAR (File 0001/2018/AFJ) under Joint Scientific Research Project ; National Natural Science Foundation of China under Grant 61972253, Grant 61672349, Grant 61672353, and Grant 61872025 ; Macao Science and Technology Development Fund under Grant 138/2016/A3 ; Fundamental Research Funds for the Central Universities, the Program of Introducing Talents of Discipline to Universities, and the China Scholarship Council State-Sponsored Scholarship Program under Grant 201806025026 ; HAWKEYE Group
KeywordIndustrial internet of things security tensor-based data mining
Abstract

In a vertical industry alliance, Internet of Things (IoT) deployed in different smart factories are similar. For example, most automobile manufacturers have the similar assembly lines and IoT surveillance systems. It is common to observe the industrial knowledge using deep learning and data mining methods based on the IoT data. However, some knowledge is not easy to be mined from only one factory's data because the samples are still few. If multiple factories within an alliance can gather their data together, more knowledge could be mined. However, the key concern of these factories is the data security. Existing matrix-based methods can guarantee the data security inside a factory but do not allow the data sharing among factories, and thus their mining performance is poor due to lack of correlation. To address this concern, in this article we propose the novel federated tensor mining (FTM) framework to federate multisource data together for tensor-based mining while guaranteeing the security. The key contribution of FTM is that every factory only needs to share its ciphertext data for security issue, and these ciphertexts are adequate for tensor-based knowledge mining due to its homomorphic attribution. Real-data-driven simulations demonstrate that FTM not only mines the same knowledge compared with the plaintext mining, but also is enabled to defend the attacks from distributed eavesdroppers and centralized hackers. In our typical experiment, compared with the matrix-based privacy-preserving compressive sensing (PPCS), FTM increases up to 24% on mining accuracy. 

Language英语
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS KeywordSYSTEM
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
Funding ProjectNational Key R&D Program of China[2018YFB1004703] ; National Natural Science Foundation of China[61972253] ; National Natural Science Foundation of China[61672349] ; National Natural Science Foundation of China[61672353] ; National Natural Science Foundation of China[61872025] ; National Natural Science Foundation of China[61861166002] ; Science and Technology Development Fund of Macau SAR[0001/2018/AFJ] ; Macao Science and Technology Development Fund[138/2016/A3] ; Fundamental Research Funds for the Central Universities ; Program of Introducing Talents of Discipline to Universities ; HAWKEYE Group ; China Scholarship Council[201806025026]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26225
Collection工业控制网络与系统研究室
Corresponding AuthorSheng H(盛浩)
Affiliation1.Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
2.Department of Electrical Engineering, Columbia University, New York
3.NY, United States
4.Beijing Advanced Innovation Center for Big Data and Brain Computing, State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Research Institute in Shenzhen, Beihang University, Beijing, China
5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shengyang, China
Recommended Citation
GB/T 7714
Kong LH,Liu, Xiao-Yang,Sheng H,et al. Federated Tensor Mining for Secure Industrial Internet of Things[J]. IEEE Transactions on Industrial Informatics,2020,16(3):2144-2153.
APA Kong LH,Liu, Xiao-Yang,Sheng H,Zeng P,&Chen, Guihai.(2020).Federated Tensor Mining for Secure Industrial Internet of Things.IEEE Transactions on Industrial Informatics,16(3),2144-2153.
MLA Kong LH,et al."Federated Tensor Mining for Secure Industrial Internet of Things".IEEE Transactions on Industrial Informatics 16.3(2020):2144-2153.
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