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Data Security and Privacy Protection Scheme Based on EC-ElGamal in Federal Learning

SN Computer Science 2025年第2期6卷 1-10页

作者： Liu, Bin Blancaflor, Eric B. School of Information Technology Mapua University Manila Philippines School of Software Engineering Xiamen University of Technology Xiamen China

Federated learning is a new distributed learning framework that can train multi-party user data without knowing the original user data, which protects user privacy to a certain extent. However, the application of federated learning in the Internet of Things still faces some problems, such as the high cost of client-server communication, frequent communication failures, and lack of high-quality data to guarantee the training process. To solve the problems of communication costs affected by different encryption schemes, dynamic user joining and exiting, and low user data quality affecting accuracy, this paper proposes a data security and privacy protection method based on EC-ElGamal in federated learning. EC-ElGamal is an additive homomorphic encryption scheme based on ElGamal elliptic curves, which has a shorter key length and less traffic than the ElGamal scheme. Firstly, the scheme uses lightweight EC-ElGamal and improved multi-key EC-ElGamal cryptosystem in the Internet of Things environment to realize cooperative training of multi-key users under federated learning and reduce communication costs. Secondly, FSOA(Federated sum optimization algorithm) is proposed to obtain high-quality data for training, which can avoid the problem of low data quality affecting accuracy. At the same time, a dynamic user joining and exiting algorithm is proposed, which can realize the dynamic user update without affecting the training accuracy. The experiment results shows that by selecting high-quality training data and a short key length encryption scheme for model training, The total encryption and decryption time of this scheme is lower than the other three encryption and decryption time of Paillier, ElGamal and LWE, the communication cost of the scheme is reduced, and the training accuracy is ensured under the condition of dynamic updating of users. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Dynamic update EC-ElGamal Federated learning Internet of things Privacy protection

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security software defined network Lifecycle management

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Research on 3D cloud modeling based on the combination of meteorological data and multiple noises 5

Research on 3D cloud modeling based on the combination of me...

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5th International Conference on Computers, information Processing and Advanced Education, CIPAE 2024

作者： Ren, He He, Xiaoxi Hu, Liang Guo, Yunhui Chen, Jinwei Wang, Min The School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331527662

In order to create a more realistic cloud simulation environment, volumetric cloud modeling combined with real meteorological data is usually considered. At present, cloud simulation based on real meteorological data basically adopts physical modeling methods, such as solving fluid dynamics equations and particle system modeling, etc. However, these methods involve a large amount of calculation for solving calculus equations, so they are often limited in the scale of drawing, and it is difficult to perform real-time simulation in large-scale scenes. The authenticity and real-time performance of volumetric cloud simulation still need to be further improved. Therefore, through the analysis of existing research, a three-dimensional volumetric cloud construction method based on meteorological data is proposed. The specific method is to generate a two-dimensional cloud map based on humidity data, and then combine the cloud map with an improved height density function to make the shape and density of the cloud change with height and meteorological data, and combine it with multiple noises to construct the shape and details of the cloud. Finally, the Raymarching algorithm is used for rendering, and finally a large-scale volumetric cloud that matches the meteorological data is constructed. This paper combines meteorological data with non-physical modeling methods effectively. Experimental results show that the volumetric cloud modeled by this method matches the cloud map generated by meteorological data, and is more realistic in shape and appearance. The cloud body modeling results have more realistic details without distortion. © 2024 IEEE.

关键词： Cloud platforms

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Design Based on Industrial Material Inspection and Defect Analysis System 5

Design Based on Industrial Material Inspection and Defect An...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Zhang, Sijie Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798331506612

The industrial material inspection and defect analysis system is designed for material quality control and fault detection in industrial production. Physical properties of the system through the acquisition of steel as the experimental data set, in combination with particle swarm algorithm to optimize ParticleSwarmOptimization, (PSO) algorithm of BP neural network (BackpropagationNeuralNetwork) artificial intelligence, the analysis of testing data and forecast, Ability to quickly identify potential defects or failure hazards in materials. At the same time, this paper also studies the state transition of steel in the process of industrial production, and proposes the state monitoring based on FiniteStateMachine (FSM) to provide timely warning and solutions for the fault types predicted by the algorithm. This research is mainly to solve the problem of automatic defect monitoring and intelligent early warning of key manufacturing links in the industry, and proposes the combination of PSO-BP neural network algorithm and finite state machine, so as to improve the detection accuracy and problem-solving efficiency, reduce the maintenance cost and production interruption risk, and ensure the safety and reliability of industrial materials. © 2024 IEEE.

关键词： Ability testing

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Research on Multi-robot Path Planning Based on Optimal Energy Consumption 5

Research on Multi-robot Path Planning Based on Optimal Energ...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Wang, Yang Kang, Jinchuan Liu, Yuqi Tang, Qi Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798331506612

Aiming at the problem of energy consumption optimization in multi-robot path planning, this paper presents a multi-robot path planning algorithm based on optimal energy consumption. First, we improve the traditional A∗ algorithm to improve the efficiency and stability of multi-robot task execution through a three-step strategy, including queue priority, conflict avoidance and parking scheduling. Then, puts forward a new OECA∗ (Optimal Energy Consumption A∗) algorithm, the algorithm integrates the multiple factors combination model of energy consumption, in order to more accurately estimate the energy consumption in the process of robot path planning in. Then, this paper verifies the performance of the improved A∗ algorithm and OECA∗ algorithm through comparative experiments, and the results show that OECA∗ algorithm has a good advantage in energy consumption. The research in this paper provides an efficient solution to the path planning and scheduling problem of multi-robot systems. © 2024 IEEE.

关键词： Microrobots

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Prediction Modeling of Propagation Delay in eLoran System Based on Meteorological Factors 5

Prediction Modeling of Propagation Delay in eLoran System Ba...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Liao, Hui Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798331506612

The study in this paper centers on the prediction of the propagation delay of the eLoran system on the Xi'an-Chengdu path, focusing on the influence of meteorological factors on the propagation delay. First, the significant correlation between meteorological factors and propagation delay is verified by correlation analysis. Subsequently, a propagation delay prediction model based on BP neural network and long short-term memory (LSTM) network is constructed, and a PSO-LSTM model combined with particle swarm optimization (PSO) algorithm is further proposed to enhance the prediction accuracy. The prediction models are evaluated and compared through experiments, and the results show that the PSO-LSTM model outperforms the other models in terms of prediction accuracy, and provides an effective solution for the prediction of the propagation delay of the eLoran system. © 2024 IEEE.

关键词： Prediction models

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Chaotic Map-Based Authentication and Key Agreement Protocol with Low-Latency for Metasystem

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Computers, Materials & Continua 2024年第3期78卷 4471-4488页

作者： Guojun Wang Qi Liu School of Electronics&Information Engineering Nanjing University of Information Science&TechnologyNanjing210044China Yancheng Polytechnic College Yancheng224000China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of SoftwareNanjing University of Information Science and TechnologyNanjingChina

With the rapid advancement in exploring perceptual interactions and digital twins,metaverse technology has emerged to transcend the constraints of space-time and reality,facilitating remote AI-based *** this dynamic metasystem environment,frequent information exchanges necessitate robust security measures,with Authentication and Key Agreement(AKA)serving as the primary line of defense to ensure communication ***,traditional AKA protocols fall short in meeting the low-latency requirements essential for synchronous interactions within the *** address this challenge and enable nearly latency-free interactions,a novel low-latency AKA protocol based on chaotic maps is *** protocol not only ensures mutual authentication of entities within the metasystem but also generates secure session *** security of these session keys is rigorously validated through formal proofs,formal verification,and informal *** confronted with the Dolev-Yao(DY)threat model,the session keys are formally demonstrated to be secure under the Real-or-Random(ROR)*** proposed protocol is further validated through simulations conducted using VMware workstation compiled in HLPSL language and C *** simulation results affirm the protocol’s effectiveness in resisting well-known attacks while achieving the desired low latency for optimal metaverse interactions.

关键词： Metasystem authentication and key agreement chaotic map secure communication

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Developing a comprehensive service quality model for online to offline e-commerce platforms using a hybrid model

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Electronic Commerce Research 2024年 1-30页

作者： Shao, QiGan Liou, James J. H. Weng, SungShun Jiang, HongBo Shao, Ting Lin, ZhenWen School of Economics and Management Xiamen University of Technology Xiamen China Department of Industrial Engineering and Management National Taipei University of Technology Taipei Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan Department of Software Engineering Xiamen Institute of Software Technology Xiamen China

One of the impacts of the COVID pandemic has been to force people to find alternates sources for daily supplies, turning to online to offline (O2O) platforms. The service quality of the O2O platform naturally affects the satisfaction which in turn has an impact on customer retention. The aim of this study is to evaluate the service quality of O2O platform to find strategies to improve consumer satisfaction. This study proposed a hybrid RS-DANP-CoCoSo model, which applies the rough set (RS) method to obtain the factors which influence the service quality of O2O platforms. The Analytic Network Process based on the Decision-making Trial and Evaluation Laboratory method (DANP) is applied to obtain the relationships and weights of the factors. The results indicate that empathy, social interaction and recommendation quality are the three most important factors. The practicability of our evaluation model is verified using the combined compromise solution (CoCoSo) method to evaluate the service quality of four O2O platforms in China. The hybrid model proposed in this study is applied to evaluate and diagnose online consumer satisfaction levels, providing personalized solutions for merchants to improve services. Finally, management implications based on the findings are also discussed. © 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Rough set theory

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Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

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Computer Modeling in engineering & Sciences 2024年第11期141卷 1827-1846页

作者： Xinchang Zhang Xiaokang Pan Rongjie Zhu Runda Guan Zhongfeng Qiu Biao Song Organization Department Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Teacher Education Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics&Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

关键词： Mesoscale eddy detection object detection contour detection remote sensing

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