文献类型：期刊文献

英文题名：Differential privacy distributed optimization algorithm against adversarial attacks for efficiency optimization of complex industrial processes

作者：Yue, Changyang[1,2];Du, Wenli[1,2];Li, Zhongmei[1,2];Liu, Bing[1];Nie, Rong[1];Qian, Feng[1,2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：62

外文期刊名：ADVANCED ENGINEERING INFORMATICS

收录：;EI(收录号：20242716653971);WOS:【SCI-EXPANDED(收录号:WOS:001266237200001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (Key Program: 62136003) , National Natural Science Foundation of China (62394345) , the Shanghai Committee of Science and Technology, China (Grant No. 22DZ1101500) and Shanghai Science and Technology Planning Program (23DZ2201700) .

语种：英文

外文关键词：Differential privacy; Mutual information; Distributed optimization; Inequality constraints; Industrial information security

摘要：In order to improve the overall performance of large-scale industrial processes with complex constraints, vast amounts of production data are collected for distributed process optimization. Usually, this data contains a lot of enterprise sensitive information. Considering the conventional distributed algorithm has the issue of data privacy leakage, which weakens the safety of the entire manufacturing process and poses a serious threat to economic benefit, this paper proposes a privacy-preserving based distributed algorithm for a class of optimization problem of large-scale industrial processes. Additionally, the proposed method can be applied to the privacy of industrial process optimization problems between different enterprises, such as industrial value chain optimization problems. In specific, the differential privacy mechanism is adopted to protect the data privacy of the local node. Meanwhile, the mutual information technique is adopted to analyze the information loss in the communication data. Moreover, Lagrange primal-dual method is used to deal with the coupling inequality constraint. Subsequently, rigorous theoretical proof shows the convergence of the proposed algorithm. Then, the algorithm privacy metrics fully demonstrate the rationality and superiority of the mutual information technology used in this article for designing privacy parameters. Finally, experimental results of numerical cases and ethylene process optimization show the effectiveness of the proposed algorithm.