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IEEE Transactions on technology and Society 2025年第1期6卷 2-14页

作者： Kolevski, David Michael, Katina Freeman, Mark University of Wollongong School of Computing and Information Technology WollongongNSW2500 Australia Newcastle University School of Business Newcastle upon TyneNE1 4SE United Kingdom Arizona State University School for the Future of Innovation in Society School of Computing and Augmented Intelligence TempeAZ85281 United States The University of Sydney Business School Business Information Systems Discipline NSW Sydney2006 Australia

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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Big Data-Driven Charging Network Optimization: Forecasting Electric Vehicle Distribution in Malaysia to Enhance Infrastructure Planning

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1020-1028页

作者： Mutian, Ouyang Maobo, Guo Tianzhou, Yu Haotian, Liu Hanlin, Yang Department of Computing UOW Malaysia KDU Penang University College Malaysia School of Business University of Wollongong Malaysia Malaysia Sydney Australia School of Information Technology Monash University Malaysia Malaysia Department of Business UOW Malaysia Selangor Malaysia

The rapid growth of electric vehicles (EVs) globally and in Malaysia has raised significant concerns regarding the adequacy and spatial imbalance of charging infrastructure. Despite government incentives and policy support, Malaysia’s charging network remains insufficient and unevenly distributed, with major urban centers having better access than rural and highway regions. This paper proposes a data-driven approach to optimize EV infrastructure planning by employing a hybrid CEEMDAN-XGBoost model for accurate EV ownership forecasting and GIS-based spatial optimization for strategic charger deployment. The model achieved superior performance compared to baseline models, with the lowest prediction errors (RMSE: 120;MAE:38;MAPE: 5.6%). Spatial analysis revealed significant infrastructure gaps in underserved regions, guiding equitable and demand-aligned station placement. The results provide valuable insights into future EV distribution and inform policy recommendations for scalable, data-driven planning across Malaysia. © (2025), (Science and information Organization). All Rights Reserved.

关键词： Hybrid vehicles

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Heterogeneous Parallel Key-Insulated Multi-Receiver Signcryption Scheme for IoV

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IEEE Transactions on information Forensics and Security 2025年 20卷 5123-5137页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu He, Debiao Chi, Chi-Hung Lam, Kwok-Yan Wuhan University School of Cyber Science and Engineering Wuhan430000 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

The rapid growth of electric vehicle and autonomous vehicle populations has led to explosive expansion of IoV data being transmitted in the wireless communication infrastructure. Advances in IoV technologies also resulted in more complex and dynamic communication protocols/patterns, which are hard for the underlying wireless network to satisfy. Besides, security considerations of IoV communications require that key management must be stringently prohibit global failure mode of key management, meaning that, if a single IoV node compromises its private key, it will not lead to total security failure of the entire IoV network. To address these issues, in this paper, we propose a heterogeneous parallel key-insulated multi-receiver signcryption scheme for IoV (HPKI-MRSC). Firstly, the proposed scheme can realize one-to-many heterogeneous transmission, in which RSUs are deployed on certificateless cryptography (CLC) system, while vehicles are allocated in identity-based cryptography (IBC) system. In this manner, we observe that message transmission efficiency is improved greatly. Secondly, the parallel key-insulated mechanism can employ two helper keys to update private key periodically, and then solve key disclosure problem. Finally, when the number of receiver n is greater than or equal to 3, the proposed scheme has a lower signcryption overhead than other comparative schemes, and thus it is more suitable for IoV. © 2005-2012 IEEE.

关键词： Receivers Public key Protocols Encryption Polynomials Vehicle dynamics Electronic mail Training Symbols Real-time systems

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Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs 6th

Route Privacy-Preserving Authentication Scheme Based on PUF...

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6th International Conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Deng, Hanwen Liu, Yining Wang, Dong School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031736988

Due to the exposure of transmitted messages in public channels within Vehicular Ad-Hoc Network (VANET), many authentication schemes have been proposed to safeguard security and privacy. And in existing schemes, to expedite the authentication process for Vehicle-to-Infrastructure (V2I) communication, vehicles requesting authentication information from Certification Authority (CA) regarding the RSUs they are expected to encounter before the journey is recommended. However, the viability of the aforementioned schemes rely on two challenging prerequisites: complete trustworthiness of CA and the assurance that the Onboard Units (OBUs), which store secret keys permanently, remain impervious to physical attacks and cloning attempts. Hence in this paper, we propose a route privacy-preserving authentication scheme based on PUF in VANETs. Basing on Oblivious Transfer (OT) method, we let CA complete authentication keys distribution without knowing the route plan of a vehicle. We utilize Physically Unclonable Function (PUF) to minimize the exposure of private key. And as the output of a PUF is easily affected by objective factors, Fuzzy Extractor (FE) is used in our scheme to correct the challenge-response pair of it. In addition, the detailed security analysis proves that our scheme can meet the security requirements. Finally, the experimental analysis suggests that our scheme is lower than the other schemes in terms of time complexity. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Vehicular ad hoc networks

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 333-353页

作者： Rao, Bosen Zhang, Jiale Wu, Di Zhu, Chengcheng Sun, Xiaobing Chen, Bing Yangzhou University School of Information Engineering Yangzhou225127 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often include sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This article begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the article thoroughly describes and compares the existing defense methods. Finally, the article concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders. © 2020 IEEE.

关键词： Data privacy

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Blockchain-Based Trust Model for Inter-Domain Routing

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Computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8 24

Pedestrian-Based Adaptive Traffic Light Control Using YOLOv8

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7th International Conference on information Science and Systems, ICISS 2024

作者： Ortega, Gerson Itliong Ordas, Amiel Mir Odiamar Villaverde, Jocelyn Flores Juanatas, Roben Antalan Juanatas, Irish Castrojeres School of Electrical Electronics and Computer Engineering Mapua University Philippines College of Computing in Information Technologies National University Philippines Department of Information Technology FEU Diliman Philippines

ISBN: (纸本)9798400717567

The advancement of YOLO and similar computer vision models represents a technological era that could revolutionize numerous aspects of day-to-day life, including traffic management. This study explores the feasibility of utilizing YOLOv8 for pedestrian detection and counting to control traffic light timing sequences at isolated pedestrian crossings. By employing the Raspberry Pi 4 Model B with a camera module, real-time pedestrian video data can be captured and processed through YOLOv8. For the hardware, three LEDs and a Dot Matrix Module replicate conventional traffic light signals and timing, effectively simulating a traffic light system. The capabilities of the custom YOLOv8 model are evaluated through pedestrian detection accuracy, calculated by percentage error across twenty samples. Results reveal an average percent error of 7.33%, indicating 92.67% accuracy in pedestrian detection and counting. These findings underscore the model’s competence for pedestrian-based adaptive traffic light control, highlighting its potential to enhance practicality and safety at pedestrian crossings by dynamically adjusting signal timings based on real-time pedestrian activity. © 2024 Copyright held by the owner/author(s).

关键词： Pedestrian safety

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A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

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IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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A Secure Protocol for Computer-Based Assessments in Disrupted Environments 5

A Secure Protocol for Computer-Based Assessments in Disrupte...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Navin, Danishka De Zoysa, Kasun Karunaratna, D.D. Harshanath, Buddika University of Colombo School of Computing Colombo Sri Lanka Sri Institute of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798331530983

Examinations are fundamental to education, yet conducting secure computer-based exams in disrupted environments presents significant challenges. This research introduces a Secure by Design protocol leveraging Delay Tolerant Networks (DTN) to overcome connectivity gaps in remote and resource-constrained areas. The proposed solution integrates physical, administrative, and technical controls to ensure the confidentiality, integrity, and availability of examination data. Through an iterative action research approach, the system evolved from a centralized Moodle server to standalone local servers, enabling offline functionality and enhanced resilience. Tested across over 180,000 candidates in Sri Lanka's largest computer-based examination, the framework effectively addressed power outages, internet disruptions, and logistical constraints. The findings demonstrate the protocol's effectiveness in promoting equitable and reliable access to education, ensuring examination continuity despite adverse conditions. © 2025 IEEE.

关键词： Delay tolerant networks

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