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5th Annual International Conference on Data Science and Business Analytics, ICDSBA 2021

作者： Xu, Min Yang, Jianhua Yang, Jian Peng, Lin Tang, Shiyang Li, Nige Hou, Zhansheng He, Zhimin Wang, Gang Wang, He Bao, Xingchuan Yu, Hai Zhu, Liang Zhang, Zehao Li, Jing Gu, Tianxiong Yang, Yang Ye, Dailiang Global Energy Interconnection Research Institute Jiangsu Nanjing210003 China State Grid ZheJiang Electric Power Co. LTD Hangzhou Zhejiang310000 China State Grid Key Laboratory of Information Network Security Nanjing210003 China

ISBN: (纸本)9781665445900

After the natural disaster attacks the power grid, a large number of alarm information will flow into the control center, especially when the power grid has complex faults or the automatic device acts abnormally. Because there are some uncertain factors in protection, circuit breaker misoperation, rejection and channel reasons, a kind of disaster assessment method that can realize uncertain information is of great significance. Although the traditional evaluation algorithm based on rough set can deal with uncertain information, it can only deal with discrete data effectively. In practice, there are continuous data and most of them are fuzzy, and the boundary between concepts is not very clear. Fuzzy set can overcome this disadvantage. This paper creatively proposes a method based on intuitionistic fuzzy rough set to evaluate power grid disasters timely and accurately. In the evaluation, attribute filtering and data cleaning are used to reduce the redundancy of disaster attributes;Through value filtering, the evaluation rules are extracted, the disaster level is determined through matching, and adjusted and updated with the development of the situation, so as to provide a scientific basis for emergency decision-making. © 2021 IEEE.

关键词： Disasters

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Trusted Identity Cross-Domain Dynamic Authorization Mechanism Based on Master-Slave Chain 10th

Trusted Identity Cross-Domain Dynamic Authorization Mechanis...

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10th International Conference on Computer Engineering and networks, CENet 2020

作者： Huang, Xiuli Guo, Qian Yao, Qigui Huo, Xuesong State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Co. Ltd. Beijing102200 China State Grid Jiangsu Electric Power Co. Ltd. Nanjing211106 China

ISBN: (纸本)9789811584619

In order to solve the security problems existing when power users access power services in different domains, this paper proposes a dynamic authorization model for power users based on master-slave chain. In this model, the slave Blockchain conducts identity authentication in each autonomous domain, and the main Blockchain undertakes identity authentication among autonomous domains. Moreover, by constructing an attribute access control model, a trusted identity cross-domain authorization mechanism is proposed, and an attribute-based allocation strategy in both single-domain and cross-domain scenarios is designed in detail. Through application analysis and security analysis, it is verified that the trusted identity cross-domain authorization mechanism proposed in this paper can be conveniently applied to existing systems, and has better performance in confidentiality, integrity, and availability. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authorization

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FedMLC: White-box Model Watermarking for Copyright Protection in Federated Learning for IoT Environment

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IEEE Internet of Things Journal 2025年

作者： Chen, Weitong Zhang, Wei Wu, Di Keskinarkaus, Anja Seppanen, Tapio Zhang, Jiale Gao, Longxiang Luan, Tom H. Yangzhou University School of Information Engineering Yangzhou225009 China ToowoombaQLD4350 Australia University of Oulu Physiological Signal Analysis Team Center for Machine Vision and Signal Analysis Oulu90014 Finland University of Southern Queensland SoMPC ToowoombaQLD4350 Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

With the widespread application of the Internet of Things (IoT), data processing has gradually migrated to edge devices that are closer to the data source. This shift has significantly improved the ability of real-time data analysis while effectively reducing bandwidth requirements and latency. Furthermore, Federated Learning (FL) has been introduced as a decentralized training method to achieve collaborative training of multiple devices while ensuring local data privacy. However, malicious clients in FL may theft trained models for unauthorized use, which causes model misuse or copyright challenges. To address these issues, this paper proposes FedMLC (Malicious client detection, Leakage tracing, and Copyright verification), a server-side white-box watermarking scheme. FedMLC utilizes the embedded watermark at different stages to achieve both traceability and copyright verification, simplifying the watermarking process. Additionally, the watermarking can also detect malicious clients in FL. Specifically, FedMLC uses the regularization term to guide the parameter signs of the normalization layer to be consistent with the watermark sign, thereby achieving watermark embedding. Experimental results show that our FL model watermarking scheme excels in malicious client detection, leakage tracing, and copyright verification, with minimal impact on model performance, able to resist various attacks such as fine-tuning, pruning, and quantization. © 2014 IEEE.

关键词： Federated learning

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Efficient and large universe ciphertext-policy attribute-based encryption with black-box traceability for ehealth

Efficient and large universe ciphertext-policy attribute-bas...

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International Conference on Cyber security Intelligence and Analytics, CSIA 2020

作者： Zhao, Jing Zeng, Peng Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Lab of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

In the modern medical environment, massive application of electronic health systems has brought us great convenience. At the same time, the large and cumbersome data storage has become easier with the emergence of cloud computing. However, the emergence of shared platforms has also brought many security issues such as data loss and privacy exposures, especially the exposures of sensitive data by illegal users. In this paper we propose an efficient and large universe ciphertext-policy attribute-based encryption (CP-ABE) scheme with black-box traceability for the collaborative eHealth system. It is of a higher efficiency by reducing greatly the cost of encryption and decryption operations. We prove the security of our new scheme under the q-parallel BDHE assumption in the standard model. © Springer Nature Switzerland AG 2020.

关键词： Sensitive data

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3D Data Fusion and Registration Technology of Substation Based on Digital Twin Space Analysis 22

3D Data Fusion and Registration Technology of Substation Bas...

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Proceedings of the 5th International Conference on information Technologies and Electrical Engineering

作者： Min Xu Xinhe Chen Zhimin Guo Lin Peng Weijian Zhang State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co.LTD China Institute of Electrical Engineering Chinese Academy of Sciences China State Grid Henan Electric Power Co. LTD China

ISBN: (纸本)9781450396806

In view of the difficulties in sharing 3D digital resources, insufficient interconnection and interaction of massive heterogeneous 3D data resources, and lack of spatial data analysis foundation for equipment diagnosis in the power grid infrastructure operation inspection stage, combined with the actual needs of the company for deepening the application of 3D digital resources and through collaboration, this paper analyzes the characteristics of typical GIM models, point cloud models and other multi-media heterogeneous models and their problems in the infrastructure construction and operation inspection applications; Aiming at the deficiency of GIM model in texture, material and illumination, the discrete sampling of equipment GIM model and its attitude registration method with scanning point cloud model are studied to estimate the spatial pose transformation relationship between heterogeneous models, so as to realize the refinement of GIM model; On the basis of attitude registration, a unified coordinate system is established to realize the comprehensive reconstruction of the multi-source medium model and the plant and station digital model combining appearance and internal, provide three-dimensional digital resource sharing for power grid operation and maintenance, technical transformation and overhaul, support the digital connection of three-dimensional capital construction and operation inspection resources, improve the comprehensive utilization level of three-dimensional digital resources of the company, and promote the transformation of power grid infrastructure acceptance and operation inspection operation mode.

关键词： Attitude registration Pose transformation Model comprehensive reconstruction

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EEG based Parkinson Detection through Supervised information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jian Song Xiang Li Wenjing Jiang Chunxiao Wang Zhigang Zhao Jialiang Lv Bin Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Qilu Hospital of Shandong University Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study presents a novel Supervised information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

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A CPK-Based Identity Authentication Scheme for IoT

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Computer Systems Science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： power Internet of Things terminal equipment identity authentication CPK PUF

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer

Parallel Optimization of Computational Fluid Dynamics Applic...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Anjun Liu Yin Liao Xihao Xu YanYan Chen Hongjun Zhong Jianming Wu Bo Kong Alexander Raikov Meng Guo Jinan Key Laboratory of High Performance Industrial Software Jinan Institute of Supercomputing Technology Jinan China Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Zhejiang Yuansuan Technology Co. Ltd Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan ) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1–2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry.

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Image super resolution via multi-regularization combining hybrid Tikhonov-TV prior and deep denoiser prior

Image super resolution via multi-regularization combining hy...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiahao Zhang Shengrong Zhao Hu Liang Changchun Wen Chen Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In a real scenario, the image is often corrupted by complex degradation, and a lot of useful information is lost, which makes super-resolution (SR) reconstruction seriously ill-posed. To effectively solve such a problem, it is crucial to correctly exploit image prior knowledge. Although existing deep learning-based methods can obtain excellent results, they cannot deal with the complex degradation effectively, which would lead to the loss of texture details and the destruction of edge details. In this paper, an efficient multi-regularization method for SR is proposed, which can simultaneously exploit both internal and external image priors within a unified framework. The hybrid Tikhonov-TV prior and deep denoiser prior are introduced to constrain the reconstruction process. That is, the proposed model combines the superiority of the piecewise-smooth prior and deep prior. Moreover, an adaptive weight parameter is employed to make the hybrid component more detail-preserving. Experimental demonstrate that the proposed method achieves better performance in image detail protection than advanced methods.

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference

Multilingual Knowledge Graph Completion via Multimodal Decou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance.

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