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Flexible Privacy-Preserving Data computing With Bilateral Access Control for Cloud-Assisted IoT

IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 3166-3178页

作者： Zhang, Yijian Chen, Jie Ning, Jianting Chu, Qiaohan Wang, Luping Zhang, Guang University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2500 Australia Wuhan University Institute for Math & AI Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science 430040 China East China Normal University Software Engineering Institute Shanghai200062 China Suzhou University of Science and Technology School of Electronic and Information Engineering Jiangsu215009 China

Cloud-assisted Internet of Things (IoT) is a new paradigm to compensate for the disadvantage of limited resources in IoT and extend the functional boundary of IoT. How to preserve the data privacy while identifying the data source in cloud-assisted IoT is a huge challenge and many cryptographic primitives such as matchmaking encryption (ME) are hence introduced to reach this goal. However, we observe that these tools fail to deal with some rigorous cases in which the data owner does not want anyone to disclose the entire data and even authorized users can only obtain a specified part. In this article, to address such issue, we design a privacy-preserving data computing system with bilateral access control (PDCS-BAC) for cloud-assisted IoT based on a new primitive called identity-based matchmaking functional encryption (IB-MFE). We give the formal definition and security model of IB-MFE, then present two concrete IB-MFE schemes for inner-product function. Thus, PDCS-BAC manages to simultaneously provide flexible privacy computation with enhanced data privacy, bilateral access control, and data authenticity. Finally, we conduct comprehensive performance evaluations to demonstrate the practicability of our schemes for cloud-assisted IoT. © 2004-2012 IEEE.

关键词： Differential privacy

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Multimodal Learning in Image Processing

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Computers, Materials & Continua 2025年第2期82卷 3615-3618页

作者： Zhixin Chen Gautam Srivastava Shuai Liu School of Educational Science Hunan Normal UniversityChangsha410081China Institute of Interdisciplinary Studies Hunan Normal UniversityChangsha410081China Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung406040Taiwan Institute of Engineering and Technology Chitkara UniversityChandigarh140401India

1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for *** is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,*** image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image *** comprehensive description of the image is required to match the computational performance of current large scale models.

关键词： Image computer LSTM

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Digital Twins and Cyber-Physical Systems:A New Frontier in Computer Modeling

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Computer Modeling in Engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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Global context-aware attention model for weakly-supervised temporal action localization

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Alexandria Engineering Journal 2025年 127卷 43-55页

作者： Fu, Weina Zhang, Wenxiang Long, Jing Srivastava, Gautam Liu, Shuai School of Information Science and Engineering Hunan Normal University Changsha41008l China Institute of Interdisciplinary Studies Hunan Normal University Changsha410081 China Department of Math and Computer Science Brandon University BrandonR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Educational Science Hunan Normal University Changsha410081 China

Temporal action localization (TAL) is a significant and challenging task in the field of video understanding. It aims to locate the start and end timestamps of the actions in a video and recognize their categories. However, efficient action localization often requires extensive precise annotations. Therefore, the researchers propose weakly-supervised temporal action localization (WTAL), which aims to locate action instances in a video using only video level annotations. The existing WTAL methods lack the ability to distinguish the action context information effectively, including the pre-action and post-action scenes, which blur the action boundary and lead to the inaccurate action location. To solve the above problems, this paper proposes a global context-aware attention model (GCAM). Firstly, GCAM designs the mask attention module (MAM) to restrict the model's receptive field and make the model focus on localized features related to the action context. It enhances the ability to distinguish the action context information and clearly locate the start and end timestamps of the actions. Secondly, GCAM introduces the context broadcasting module (CBM), which supplements the global context information to keep the features intact in temporal dimension. This module solves the issue that the model overemphasizes the localized features due to the addition of the MAM. Extensive experiments on the THUMOS14 and ActivityNet1.2 datasets demonstrate the effectiveness of GCAM. On the THUMOS14 dataset, GCAM achieves an average mean average precision (mAP) of 49.5 %, representing a 2.2 % improvement over existing WTAL methods. On the ActivityNet1.2 dataset, GCAM achieves an average mAP of 27.2 %, representing a 0.3 % improvement over existing WTAL methods. These results highlight the superior performance of GCAM in accurately localizing actions in videos. © 2025 The Authors

关键词： Temporal action localization Weakly-supervised Learning Global context-aware Mask attention Action context

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Block-FeST: Blockchain-Enhanced Federated Sparse Transformers for Privacy-Preserving RES Forecasting in Internet of Vehicles Systems

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IEEE Internet of Things Journal 2025年

作者： Hameed, Aroosa Danish, Syed Muhammad Ranjha, Ali Srivastava, Gautam Carleton University Department of Systems and Computer Engineering Ottawa Canada Algoma University School of Computer Science and Technology Brampton Canada University of the Fraser Valley School of Computing BC Canada Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

Internet of Vehicles (IoV) frameworks integrate smart vehicles, roads, network infrastructures, and users into one system, enhancing environmental awareness, increasing efficiency, and reducing accidents. Although IoV is widely adopted, it has resulted in an increase in global energy demand, necessitating more robust and reliable energy solutions. In order to meet this growing demand, both traditional and distributed energy generation technologies have been developed, particularly renewable energy sources (RES). In order to ensure seamless operation of smart vehicles and related infrastructure, integrating renewable energy into existing grid infrastructure is essential. This enables stable and efficient power supply to IoV systems, especially during time periods of high demand. As part of this shift, it is important to accurately forecast the energy generation load of individual prosumers—entities that both produce and consume energy—because of their intermittent and dynamic nature. Therefore, we propose Block-FeST, a blockchain-based Federated Learning (FL) framework designed to predict the energy generation patterns of RES prosumers while preserving their private and sensitive data. Within this Block-FeST framework, a Sparse Transformer model is used to forecast energy generation among prosumer clients. Additionally, blockchain technology is integrated into the Block-FeST framework to enable distributed aggregation and securely validate and record the local parameters shared by clients. The results indicate that Block-FeST is superior to the second-best baseline method, with improvements of 20.4% in the mean square error (MSE), 13.7% in mean absolute error (MAE) and 19.3% in root mean square error (RMSE) for a long sequence length of 128. © 2014 IEEE.

关键词： Federated learning

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Federated Multi-Agent Reinforcement Learning for Resource Allocation in NR-V2X Mode 2

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IEEE Internet of Things Journal 2025年

作者： Saad, Malik Muhammad Tariq, Muhammad Ashar Ajmal, Mahnoor Kim, Dongkyun Srivastava, Gautam Kyungpook National University School of Computer Science and Engineering Dongseo University Republic of Korea and the School of Computer Science Busan Daegu Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

The Third Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the Semi-persistent Scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the Physical Sidelink Feedback Channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride towards efficient and conflict-resilient resource allocation in vehicular communications. © 2014 IEEE.

关键词： Resource allocation

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CuckooAttack: Towards Practical Backdoor Attack against Automatic Speech Recognition Systems

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Bowen Ge, Yunjie Fang, Zheng Wang, Tao Zhao, Lingchen Lu, Quan Jiang, Ning Wang, Qian Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University Institute for Math&AI Wuhan430072 China Mashang Consumer Finance Co. Ltd. Chongqing400000 China

Deep learning-based automatic speech recognition (ASR) systems are capable of transcribing input audio of arbitrary duration into character sequences, which are widely used in daily life. However, recent research has found that deep learning models are vulnerable to backdoor attacks. A malicious adversary can embed a backdoor functionality into the model during the training phase and manipulate the output of the backdoored model by adding a specific trigger to the input during the inference phase. Unfortunately, TrojanModel, the existing state-of-the-art backdoor attack against ASR systems (Zong et al. S&P'23), relies on an overly strong assumption that requires the adversary to modify the model structure beyond data poisoning, which significantly limits its practicability. In this paper, we propose CuckooAttack, a more practical backdoor attack against ASR systems that only requires poisoning a small portion of the training data. We first construct a phoneme-level auxiliary dataset to generate effective, robust, and unnoticeable triggers, while substantially lowering computational expenses. Considering the real-world ASR application scenarios, we propose an adaptive trigger injection mechanism to ensure that the backdoor can be activated on variable-duration input audio under asynchronous temporal conditions. To further enhance the efficacy of CuckooAttack, we design a character-filling strategy tailored for ASR to construct poisoned samples, which facilitates the model in establishing backdoor connections. Extensive experiments show that CuckooAttack achieves comparable performance with TrojanModel under a weaker assumption. Specifically, CuckooAttack achieves an attack success rate of about 99% in the digital domain and over 90% in the physical domain, with a poison rate of only 1%. © 2004-2012 IEEE.

关键词： Deep learning

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GPT-Based Automated Induction: Vulnerability Detection in Medical Software

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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A novel pythonic paradigm for image encryption using axis-aligned bounding boxes

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Scientific Reports 2025年第1期15卷 1-27页

作者： Banga, Abdulbasid Abbas, Akifa Ali, Danish Innab, Nisreen Alluhaidan, Ala Saleh Iqbal, Nadeem Diab, Hossam College of Computing and Informatics (CCI) Saudi Electronic University Riyadh 11673 Saudi Arabia School of Computing Bath Spa University Academic Center Ras Ul Khaima United Arab Emirates Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh 13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Computer Science & IT The University of Lahore Lahore 54000 Pakistan Math and Computer Science Department Faculty of Science Menoufia University Menoufia Shibin El Kom 32511 Egypt Computer Science Department Applied College Taibah University Madinah 41477 Saudi Arabia

In the digital age, images permeate every facet of our lives, often carrying critical information for organizations, institutions, and even nation-states. Ensuring their security against unauthorized access is paramount. This research introduces a novel image encryption algorithm designed to safeguard the integrity and confidentiality of sensitive gray-scale digital images. The algorithm leverages the theory of axis-aligned bounding boxes, translating the input image into a 3D representation. Within this 3D space, two identically sized boxes are generated and assessed for overlap. If the boxes do not overlap, the pixels within them are swapped. This pixel-swapping process, guided by random numbers generated from a 5D multi-wing hyper-chaotic map, is repeated numerous times to infuse confusion into the image. To further enhance security, diffusion effects are introduced by performing an XoR operation between the confused image and random numbers provided by the piece-wise linear chaotic map. This study employs private key cryptography and utilizes four gray-scale images to validate the feasibility and effectiveness of the proposed method. Simulation has been carried out in the Pythonic ecosystem. So, the algorithms presented in this study are designed to closely resemble Python code. Comprehensive validation metrics attest to the robustness of the cipher, achieving an information entropy of 7.99985 and a computational speed of 0.3987 seconds. These results underscore the potential of this encryption approach for practical, real-world applications. These applications span military, diplomacy & government, commerce, showbiz, industry, and social life etc. © The Author(s) 2025.

关键词： Chaotic map Non-linearity Random numbers S-Box Smart cities Wireless communication security

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Hybrid Optimization for NOMA-Based Transmissive-RIS Mounted UAV Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Zain Asif, Muhammad Khan, Wali Ullah Elfikky, Abdelrahman Ihsan, Asim Ahmed, Manzoor Ranjha, Ali Srivastava, Gautam Electrical and Computer Engineering Department Baskin School of Engineering University of California Santa Cruz United States School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China University of Luxembourg Luxembourg City1855 Luxembourg Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom School of Computer and Information Science Hubei Engineering University Xiaogan City432000 China Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan City432000 China Ecole de Technologie Supérieure MontréalQCH3C 1K3 Canada Department of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

In this work, we introduce a novel hybrid joint optimization framework specifically designed for enhancing the performance of consumer electronics in vehicular networks using a transmissive reconfigurable intelligent surface (T-RIS)-mounted unmanned aerial vehicle (UAV) system. The UAV employs the non-orthogonal multiple access (NOMA) protocol to broadcast data to multiple ground devices, ensuring efficient communication. Our primary objective is to maximize the overall system sum rate while adhering to key constraints such as the rate requirements of ground devices, UAV battery capacity, and UAV coordinate boundaries. The optimization challenge of maximizing the system's sum rate is inherently non-convex and complex. To address this, we decompose the problem into manageable subproblems. The beamforming optimization problem is tackled using successive convex approximation and semi-definite programming techniques, allowing for effective handling of non-convexity. For power allocation, we employ the Lagrangian dual method along with the sub-gradient technique, ensuring optimal power distribution among devices. To optimize the UAV's location, we propose a dueling-based double deep reinforcement learning (D3RL) framework. This approach effectively combines all computed solutions, resulting in a comprehensive joint optimization strategy. Simulation results highlight the exceptional performance of the proposed framework. Specifically, optimizing the UAV's location leads to a substantial performance gain of up to 65.9% compared to a system where only beamforming and power allocation are optimized with the UAV positioned at the center of the service area. These findings underscore the potential of our framework in advancing consumer electronics connectivity in vehicular networks. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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