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TRE-DSP: A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Zhou, Yousheng Peng, Rundong Liu, Yuanni Vijayakumar, Pandi Gupta, Brij College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China College of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering University College of Engineering Melpakkam Tamil nadu Tindivanam604001 India Department of Computer Engineering National Institute of Technology Kurukshetra India

With the popularity of the Internet of Vehicles (IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV (TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV. © 2024

关键词： Information leakage

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Design and performance evaluation of HfS2/AlSe heterostructure for enhanced photocatalytic water splitting

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International Journal of Hydrogen Energy 2025年 130卷 262-270页

作者： He, Lijun Mi, Cheng Huang, Qijun Wang, Liyan Ma, Kang She, Liang Yu, Mi Qin, Yuhang Yang, Peixuan The School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Integrated Circuit Collaborative Innovation Center No. 36 Xiyong Avenue Shapingba District Chongqing400065 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

In this study, a HfS2/AlSe heterostructure was constructed and its photocatalytic performance was systematically evaluated. The complementary positions of the band edges of the HfS2 and AlSe monolayers enable more effective separation of photogenerated charge carriers and enhancement of the photocatalytic water splitting efficiency. First-principles calculations and simulations identify the heterostructure with the lowest binding energy, confirming its feasibility and stability for photocatalytic water splitting. The built-in electric field enables the rapid recombination of electron-hole pairs at CBM and VBM, while the hydrogen evolution reaction and oxygen evolution reaction can separately be achieved at the AlSe and HfS2 layers to complete the overall water splitting. Furthermore, the calculated solar hydrogen production (STH) efficiency of 14.94 % is significantly better than other heterostructures. The HfS2/AlSe heterostructure exhibits excellent light absorption in the visible and UV ranges, and strain engineering is used to tuning its electronic structure, which leads to the redshift and blueshift of light absorption, optimizing its photocatalytic performance. The heterojunction exhibits high carrier mobilities (the hole mobility is 1513 cm2s−1V−1 in the x direction and 748 cm2s−1V−1 in the y direction). © 2025 Hydrogen Energy Publications LLC

关键词： Heterojunctions

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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GDP-YOLO: Incorporating Gather-and-Distribute Mechanism into Pruning Strategy for Object Detection 24

GDP-YOLO: Incorporating Gather-and-Distribute Mechanism into...

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2024 International Conference on Virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Yuan, Zhengwu Zhang, Fan Tang, Peixian Ceng, Fanchang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400710926

In addressing the challenges of low detection accuracy and substantial model parameters in traditional object detection networks, this paper proposes an enhanced algorithm, GDP-YOLO (Incorporating Gather-and-Distribute Mechanism into Pruning Strategy, based on YOLOv8). By incorporating the Gather-and-Distribute Mechanism into the pruning strategy, high-accuracy detection is maintained while computational complexity is reduced. This mechanism collects and fuses information from various levels using a unified module, effectively minimizing the inherent information loss found in traditional FPN structures and enhancing the information fusion capability of the neck structure without significantly increasing latency. In the detection head, a new dynamic head framework is introduced, integrating the target detection head with attention mechanisms. Through the application of multi-head attention across scale, spatial, and task perceptions, the functionality of the target detection head is significantly enhanced. Additionally, to reduce the computational load, the Lamp pruning strategy is employed for unstructured pruning, effectively removing low-scoring individual weights and substantially compressing the model without sacrificing accuracy. Experimental results on the Pascal VOC 2012 test set reveal that GDP-YOLO surpasses the original model, reducing parameters and computational demands by approximately 60%, and enhancing mAP by 2.4% and 3.0%, respectively. Significant improvements in other metrics also demonstrate the algorithm's strong practical applicability. © 2024 Copyright held by the owner/author(s).

关键词： Information fusion

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HP-CSF: An GPU Optimization Method for CP Decomposition of Incomplete Tensors 20th

HP-CSF: An GPU Optimization Method for CP Decomposition of I...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Liu, Guanxiong Wu, Hao College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Southwest University Chongqing400715 China

ISBN: (纸本)9789819628292

A tensor is multi-dimensional generalization of a vector and a matrix in high-dimensional space. Tensor decomposition can represent a high-order tensor into many low-dimensional factors, which can reduce computation and storage complexity during data processing. Canonical Polyadic (CP) decomposition is one of the most widely used decomposition frameworks, which decomposes a N-order tensor into N factor matrices via operating Matricized Tensor Times Khatri-Rao Product (MTTKRP) computation. However, the MTTKRP is the computational bottleneck for algorithms computing CP decomposition of incomplete tensors due to intermediate product of feature matrices. To reduce the computation complexity of MTTKRP, this paper presents Hyperparameter-CSF (HP-CSF). Its main idea is to increase the balance of load at thread-level and set up an allocation rule for parallel deployment. Compared to state-of-the-art works, our method achieves 100.7% speedup across NVIDIA Geforce RTX 4090 on a range of sparse tensors. © IFIP International Federation for Information Processing 2025.

关键词： Tensors

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Tensor-based multigraph structure preserving embedding for dimensionality reduction of hyperspectral images 3

Tensor-based multigraph structure preserving embedding for d...

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3rd International Conference on Geographic Information and Remote Sensing technology, GIRST 2024

作者： Shi, Guangyao Chen, Feng Xiang, Wenhao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510689077

An important solution to the curse of dimensionality problem in large-volume, multi-band hyperspectral data is to perform the dimensionality reduction (DR) through graph embedding (GE) techniques. In this paper, a novel DR method called tensor-based multigraph structure preserving embedding (TMSPE) is proposed for HSI classification. At first, TMSPE defines a multi-graph structure with pixel-sharing weights, which can integrate the spatial-spectral information within the tensor samples. Then, it selects effective neighbor points for multigraph construction based on the probability distribution of different samples, which ensures than all nodes in the multigraph structure have correct connection relationships. Finally, the spatial coordinate relationships between pixels are introduced to adjusts the connection weights between different pixels adaptively, which can enhance the effectiveness of manifold reconstruction for HSI data. The performance of TMSPE is evaluated on PaviaU dataset, and experimental comparisons with several classical algorithms demonstrate the superiority of the proposed TMSPE algorithm in terms of classification accuracy. © 2025 SPIE.

关键词： Pixels

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Hyperspectral Imagery Classification Based on Multi-scale Neighborhood Structure Tensor Representation

Hyperspectral Imagery Classification Based on Multi-scale Ne...

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2024 International Conference on Remote Sensing and Digital Earth, RSDE 2024

作者： Shi, Guangyao Chen, Feng Xiang, Wenhao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (数字)9781510688223

ISBN: (纸本)9781510688216

A major challenge in the field of hyperspectral image (HSI) analysis is how to effectively use spatial-spectral information to improve land cover classification accuracy when labeled training samples are limited. In this paper, a novel HSI classification method termed multi-scale neighborhood structure tensor representation classification (MNSTRC) was proposed. At first, a tensor-based neighborhood structure representation model is designed for HSI, which can reveal the representation relationships between tensor samples from different feature dimensions. Then, it adopts the minimum representation error of different orders as the overall model error, which can enhance the model's representation capability for tensor samples. Finally, based on the scale variability of tensor samples, the classification results of different scales are integrated to further improve the classification performance. The performance of MNSTRC is evaluated on two hyperspectral datasets, and the experimental results show that the proposed MNSTRC method can obtain higher classification accuracies when compared with some state-of-the-art methods. © 2025 SPIE.

关键词： Land cover Statistical modeling Hyperspectral imaging Image classification Data modeling Statistical analysis

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Overlapping community-based fair influence maximization in social networks under open-source development model algorithm

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Artificial Intelligence Review 2025年第8期58卷 1-31页

作者： Wei, Pengcheng Yan, Bei Huang, Sixing Zhou, ZhiHong School of Artificial Intelligence Chongqing University of Education Chongqing400065 China School of Mathematics and Big Data Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Jilin Normal University Jilin Siping136000 China

The aim of Influence Maximization (IM) in social networks is to identify an optimal subset of users to maximize the spread of influence across the network. Fair Influence Maximization (FIM) develops the IM problem with the aim of equitable distribution of influence across communities and enhancing the fair propagation of information. Among the solutions for FIM, community-based techniques enhance performance by effectively capturing the structural properties and ensuring a more equitable influence spread. However, these techniques often ignore the overlapping nature of communities and suffer from a trade-off between complexity and fairness. With this motivation, this study handles the FIM based on Overlapping Community detection under optimization algorithms (FIMOC). FIMOC includes an overlapping community detection approach that can consider the importance of influential overlapping nodes in communities. Meanwhile, FIMOC uses a non-overlapping and overlapping node selection module based on communities to identify potential candidate nodes. Subsequently, FIMOC uses the Open-Source Development Model Algorithm (ODMA) as an optimization algorithm to identify the set of influential nodes. Our method considers the dynamic and overlapping nature of social communities, ensuring that the influence spread is not only maximized but also equitably distributed across diverse groups. By leveraging real‐world social networks, we demonstrate the effectiveness of our method compared to state-of-the-art methods through extensive experiments. The results show that our method achieves a more balanced influence spread, providing a fairer solution, while also enhancing the overall reach of information dissemination. © The Author(s) 2025.

关键词： Open access

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Improved quantum image weighted average filtering algorithm

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Quantum Information Processing 2025年第5期24卷 1-24页

作者： Yuan, Suzhen Li, Xianli Yinxia, Shu Qing, Xianrong Deng, Jermiah D. College of Opto Electronic Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Computing University of Otago Dunedin Dunedin9054 New Zealand School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

Average filtering plays a vital role in image smoothing tasks. However, existing quantum image weighted average filtering methods suffer from high circuit complexity. Therefore, this paper proposes an improved quantum color image weighted average filtering algorithm and its corresponding quantum circuit. First, we improve the quantum circuit to prepare classical color images into a quantum state. Then, an improved quantum divider is developed, and a weighted average filter is constructed using basic quantum image processing modules. Next, to enhance the universality of the filter, a quantum comparator with lower circuit complexity is used to design a noise detection module for distinguishing noise from real signals. Finally, a quantum circuit for color image weighted average filtering is designed, and simulations are conducted on the IBM Quantum Experience (IBM Q) platform to verify the feasibility of our algorithm. The analysis shows that compared with existing methods, this method significantly reduces the circuit complexity and has better filtering performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image enhancement

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Efficient and Privacy-Preserving Authentication for Federated Learning in Industrial Internet of Things Data Sharing Application

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IEEE Internet of Things Journal 2025年第9期12卷 11652-11663页

作者： Huang, Yongfei Xu, Guangxia Wang, Qiuyan Song, Xiaoling Wang, Xinhui Hengyang Normal University College of Computer Science and Technology Hengyang421002 China Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Hunan Institute of Technology School of Electrical and Information Engineering Hengyang421002 China

Federated learning (FL) has emerged as a promising approach for collaborative machine learning while respecting data privacy. However, directly aggregating local models during FL training can expose sensitive information, as adversaries may analyze model parameters to infer the underlying raw data. To address this concern, implementing a robust identity authentication mechanism can verify the integrity of local models and confirm their sources. However, many existing authentication schemes are computationally intensive, which can hinder performance. In this article, we present a lightweight authentication scheme for FL called EPPAFL. This innovative approach is compatible with all machine learning algorithms, maintains data utility, and ensures strong privacy protection. Additionally, it can be integrated with detection mechanisms to defend against data poisoning and free-rider attacks. Specifically, we first design a certificateless signature scheme based on the elliptic curve cryptography (ECC) and then propose an anonymous and efficient authentication protocol based on the Chinese remainder theorem (CRT), which supports batch verification, dynamic updates, and revocations of group keys. Moreover, participants interact anonymously to achieve secure communication, and trusted authority (TA) can track and recover the real identities of participants in case of malicious activities. We provide formal security proofs and analyses to demonstrate the achieved security properties. Performance evaluations demonstrate that our proposed protocol significantly reduces the costs associated with signature generation and verification, outperforming the existing schemes in both communication and computational efficiency. © 2014 IEEE.

关键词： Federated learning

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