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Protecting Your Attention during Distributed Graph Learning: Efficient Privacy-Preserving Federated Graph Attention Network

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1949-1964页

作者： Zhou, Jinhao Wu, Jun Ni, Jianbing Wang, Yuntao Pan, Yanghe Su, Zhou Waseda University Graduate School of Information Production and Systems Fukuoka808-0135 Japan Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats. © IEEE.

关键词： Differential privacy

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An Extended Euclidean Algorithm Implementation over Ciphertext Space 2nd

An Extended Euclidean Algorithm Implementation over Ciphert...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chi, Po-Wen Lin, Yuan-Pang Wang, Ming-Hung Chuang, Yun-Hsin Department of Computer Science and Information Engineering National Taiwan Normal University Taiwan Taipei Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Chiayi Taiwan Engineering Management Graduate Program College of Engineering National Cheng Kung University Taiwan Tainan Taiwan

ISBN: (纸本)9789819777853

Fully homomorphic encryption (FHE) is a useful cryptographic primitive. It makes a third-party manipulate data in the ciphertext space and therefore is a key technology for privacy-preserving outsourcing services. Theoretically, FHE can evaluate any circuits. However, the implementation is not trivial. The reason it lacks lots of basic operations like conditional check and branch control. In this paper, we focus on extended Euclidean algorithm operation over FHE. We introduce the problems we encounter and show how we solve these issues. We implement our design and evaluate its performance on a real computer. Finally, we propose some research directions for our future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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Comparative Study of Fresh Produce Supply Chain Models Under Contract Farming and Cooperative Markets

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Journal of Advanced Computational Intelligence and Intelligent Informatics 2025年第3期29卷 480-488页

作者： Huang, Yameng Hasuike, Takashi Graduate School of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Department of Industrial and Management Systems Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan

Smallholders play a vital role in global agriculture but face challenges such as resource limitations and market uncertainties. Contract farming (CF) and cooperative markets provide opportunities to smallholders to address these challenges. In this context, this study develops mathematical planning models for fresh produce supply chains (FPSCs) in two scenarios involving CF and cooperative markets with the objective of maximizing profits of smallholders. Results of computational experiments and sensitivity analysis reveal that CF has greater potential than cooperative markets to mitigate price volatility, thereby offering a stable income source to smallholders. This study provides valuable insights into CF and cooperative market practices, contributing to the sustainability of FPSCs and improving smallholders’ livelihoods. © Fuji Technology Press Ltd.

关键词： Heuristic algorithms

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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Computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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Combination of SKU in POD Assignment in Robotic Mobile Fulfillment systems 11th

Combination of SKU in POD Assignment in Robotic Mobile Fulfi...

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11th International Conference on Industrial engineering and Applications, ICIEA 2024

作者： Pratiwi, Dinda Tria Chou, Shuo-Yan Suparno Kurniati, Nani Department of Industrial Management National Taiwan University of Science and Technology Keelung Taipei Taiwan Department of Industrial and Systems Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

ISBN: (纸本)9789819764914

The Robotic Mobile Fulfillment System (RMFS) is widely used in e-commerce warehouses and includes pods, storage locations, workstations for order picking or replenishment, and mobile robots. Decision-making in warehouse systems can be strategic, tactical, or operational. Product assignment, a tactical decision, significantly impacts picking efficiency. This research concentrates on SKU-to-pod allocation, the preliminary step before simulation, involving two phases: product grouping and product combination. Effective product grouping can enhance picking efficiency, while product combination in pod allocation aims to optimize the units picked per pod, termed pile-on, thereby reducing the reliance on Automated Mobile Vehicles (AMVs). Three scenarios were examined: Random Baseline, Class Combination, and Cluster Combination. Class Combination employs ABC classification to sort SKUs into classes using Pareto’s principle, correlating SKU percentages with order frequency percentages. In contrast, Cluster Combination considers product dimensions for pod placement. Simulations determine the optimal pile-on by evaluating the units picked per pod visit to the pick station, with a higher unit count per visit indicating reduced mobile robot transport and increased efficiency. The simulations revealed that Cluster Combination, the final scenario, achieved the best pile-on, with improvements of 30.82% and 8.19% over the first two scenarios, respectively. These results were validated using one-way ANOVA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile robots

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Machine-learning-based sampling inspection under OQC capacity for real-time quality monitoring in the TFT-LCD industry

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International Journal of Production Research 2025年第6期63卷 2090-2113页

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Chiang, Chih-Hsiung Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei Taiwan Division of Quality Control AUO Corporation Taichung Taiwan

In the thin film transistor-liquid crystal display (TFT-LCD) industry, the competition is fierce, and the external failure cost is high. To improve the quality of released products, sampling inspection under outgoing quality control (OQC) is conducted before products are released. Owing to the variety and complexity of inspection items, the task must be carried out manually while realising automation is difficult. Traditional random sampling inspection is limited by sampling quantity, manpower, and material resources. To effectively improve acceptable quality levels, this paper proposes a quality predictive monitoring framework tailored to the production and inspection characteristics of the TFT-LCD industry. This framework includes real-time automatic quality monitoring based on an optimal machine learning prediction model with two-phase feature creation, and it also addresses OQC inspection capacity constraints. Experimental results show the prediction performance based on proposed model is better than the traditional random sampling process. In practice, superior average outgoing quality improves by more than 70% at the same sampling level. In addition, the features of the process can be further controlled and managed based on the prediction model to monitor the key parameters in real time and respond to abnormalities quickly, thereby further improving the quality of released products. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liquid crystal displays

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A decentralized and privacy-preserving framework for electronic health records using blockchain

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Alexandria engineering Journal 2025年 126卷 196-203页

作者： Alahmari, Saad Alshardan, Amal Al-Wesabi, Fahd N. Sorour, Shaymaa Alghushairy, Omar Alsini, Raed Khadidos, Alaa O. Duhayyim, Mesfer Al Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj16273 Saudi Arabia

As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted. © 2025 The Authors

关键词： Electronic health record

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Empowering self-sustained agriculture: an IoT field tracking model driven by fog computing

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Singh, Gurpreet Singh, Jaspreet Juneja, Sapna Gulzar, Yonis Gupta, Deepali Ghafoor, Kayhan Zrar Kumar, Mukesh Department of Computer Science and Engineering Chandigarh University Punjab Chandigarh140413 India KIET Group of Institutions Delhi-NCR Uttar Pradesh Ghaziabad201206 India Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Information and Communication Technology Eng Erbil Polytechnic University 42VQ+756 120 Meter St Erbil Erbil Governorate Iraq Mechanical Engineering College of Engineering Assosa University Assosa5220 Ethiopia

Increasing availability of cost effective IoT (Internet of Things) devices have led to the development of IoT- based farm management systems. Smart Agriculture is a demanding approach that employs the use of technology-enabled information and communication to monitor farms. In India, somewhere around 60% of the organization’s operations mostly dependent on agriculture directly or indirectly. A nation’s advancement is typically influenced by concerns with agriculture. The most effective approach to addressing agriculture’s challenges is always to improve traditional agricultural practices as well as modernize them into smart agriculture. The smart agriculture technique offers beneficial data collection, highly precise control as well as automated monitoring strategies. This research proposed a IoT assisted fog-based model that promises to provide the cost-effective smart agriculture system. Fog platform brings computation and storing nearer at the network’s the border, allowing for the efficient processing of data produced by IoT devices across smart agricultural systems. The bibliometric evaluation of the Web of Science database for the adoption of the IoT-Fog base infrastructure in the creation of smart agriculture systems is also highlighted in this paper. The proposed solution when implemented as well as when contrasted with existing IoT-based agriculture system exhibit better efficiency. The result revealed that proposed system brings down response time, error rate and standard deviation to significant level and improves the accuracy of current system exponentially. © The Author(s) 2025.

关键词： Agricultural robots

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Current advances in bioethanol synthesis from lignocellulosic biomass: sustainable methods, technological developments, and challenges

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Journal of Umm Al-Qura University for Applied Sciences 2025年 1-18页

作者： Novia, Novia Melwita, Elda Jannah, Asyeni Miftahul Selpiana, Selpiana Yandriani, Yandriani Afrah, Bazlina Dawami Rendana, Muhammad Department of Chemical Engineering Faculty of Engineering Universitas Sriwijaya Jl. Raya Palembang-Prabumulih KM 32 Inderalaya Sumatera Selatan Ogan Ilir 30662 Indonesia Master Program of Environmental Management Graduate School Universitas Sriwijaya Jl. Padang Selasa 524 Bukit Besar Sumatera Selatan Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya South Sumatra Palembang 30139 Indonesia

Bioethanol production from lignocellulosic biomass has become an economical and environmentally friendly substitute to current petroleum-based fuels. This is due to its ability to decrease carbon dioxide emissions and make use of plentiful natural resources. Current advances in this area concentrate on improving the effectiveness and expandability of the conversion procedures by using inventive pretreatment approaches, enhanced enzyme compositions, and refined fermentation technologies. Consolidated bioprocessing (CBP) and utilizing genetically modified microbes have expedited the decomposition of intricate biomass complexes and enhanced overall productivity. Furthermore, process intensification developments, such as the adoption of hybrid reactors and continuous production systems, have considerably decreased production expenses and energy usage. Nevertheless, there are still obstacles to overcome, including the raw materials inconsistency, the demand for efficient recovery and purifying techniques, and the financial viability of extensive processes. This study examines the latest developments in bioethanol production from lignocellulosic biomass. It focuses on ecological approaches, technical advancements, and the main obstacles that need to be overcome to exploit its promise as a biofuel fully. This review primarily focused on outcomes documented in the last five years from 2019 to 2024. The first segment of this work focuses on the second-generation bioethanol production process, which includes: the properties and composition of lignocellulosic biomass, pretreatment, enzymatic saccharification, and fermentation. The subsequent portion of this paper examines a techno-economic analysis, the obstacles and the prospective technology outlook. It finishes by discussing findings from research fields that haven't been investigated yet, besides the obstacles faced in bioethanol manufacturing methods. © The Author(s) 2025.

关键词： Bioethanol Enzymatic hydrolysis Fermentation Lignocellulosic biomass Pretreatment

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Multi-Scale Temporal Analysis for Failure Prediction in Energy systems 71

Multi-Scale Temporal Analysis for Failure Prediction in Ener...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Le, Anh Huynh, Phat K. Yadav, Om P. Le, Chau Pirim, Harun Le, Trung Q. Department of Civil Engineering North Dakota State University 1340 Administration Ave FargoND58102 United States Department of Industrial and Systems Engineering North Carolina A&T State University 1601 E. Market Street 408 McNair Hall GreensboroNC27411 United States Department of Industrial and Systems Engineering North Carolina A&T State University 1121 E. Market Street 408 McNair Hall GreensboroNC27411 United States Department of Engineering Technology & Construction Management University of North Carolina at Charlotte 9201 University City Boulevard CharlotteNC28223 United States Department of Industrial and Manufacturing Engineering North Dakota State University 1315 Centennial Boulevard Offerdahl East Rm 106 FargoND58102 United States Department of Industrial & Management Systems Engineering University of South Florida 4202 East Fowler Ave TampaFL33620 United States

ISBN: (纸本)9798350367744

Many previous studies on power networks focus on identifying disturbances but they often miss how the system behaves across different time scales. Many existing models struggle to predict these systems’ complex, nonlinear behavior of these systems during extreme weather conditions. This study proposes a multi-scale system temporal analysis for failure prediction in energy systems, focusing on predicting failures in electric power networks using Phasor Measurement Unit (PMU) data. The model integrates multi-scale analysis with advanced machine learning techniques to capture both short-term and long-term behavior. By analyzing disturbances across multiple temporal scales, we aim to improve failure prediction accuracy in complex energy systems. One of the main challenges with PMU data is the lack of labeled states, despite the availability of disturbance times logged as failure records. This limitation makes distinguishing between normal, pre-disturbance, and post-disturbance conditions difficult. We apply multi-scale system dynamics modeling to address this issue and extract key predictive features from PMU data for failure prediction. First, multiple domain features were extracted from the PMU data from time series data. Second, a multi-scale windowing approach was applied, using window sizes of 30s, 60s, and 180s to capture both short-term and long-term system dynamics. This multi-scale analysis allowed the model to detect patterns across different time scales. Third, feature selection was performed using Recursive Feature Elimination (RFE) to reduce the dimensionality of the dataset and identify the most important features. We selected RFE as the best feature selection for its ability to systematically eliminate less important features, thus improving model interpretability and reducing complexity. This step was crucial for identifying the most relevant features for predicting disturbances. Fourth, these features were used to train multiple machine-learning models.

关键词： Phasor measurement units

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