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A Traceable Privacy-Preserving Transaction Protocol With Evolutionary Threshold Authentication

IEEE Internet of Things Journal 2025年

作者： Xie, Ninghai Li, Jiguo Lin, Chao Zhang, Yichen Shen, Jian Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Guangzhou University Guangdong Key Laboratory of Blockchain Security Guangzhou510006 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Decentralized payment systems, such as Bitcoin, enable immutable and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this paper, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation. © 2014 IEEE.

关键词： Authentication Protocol

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Highway Gates Dynamic Adaptation network For Knowledge Graph Entity Alignment 21st

Highway Gates Dynamic Adaptation Network For Knowledge Graph...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Yusuf, Nursharbat Yilahun, Hankiz Imam, Seyyare Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi830017 China School of National Security Studies Xinjiang University Urumqi830017 China

ISBN: (纸本)9789819601240

The Entity alignment aims to align entities across different Knowledge Graphs and plays an important role in knowledge fusion tasks. In recent years, existing methods have typically used multiple aspects of information to enhance the accuracy of matching, which makes the matching process more comprehensive. However, existing methods that integrate multiple features have relied on fixed weighting strategies, making it challenging to adjust the relative importance of each feature. Therefore, this paper proposes an entity alignment framework(HGEA) based on highway gates dynamic adaptive network. Firstly, HGEA aggregates the structure, name, and node importance of entities. Among them, to fully utilize of the structural information of the Knowledge Graphs, we propose an entity and relation representation strategy based on a graph attention network. Then, we have a highway gates dynamic adaptation network, which combines the gating mechanism and multi-level self-attention to dynamically adjust the weight of each feature to adapt to the information flow of different entity features and optimize the entity alignment. Experiments on three real-world cross-lingual datasets demonstrate that our framework surpasses the current state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Document-Level Relation Extraction Model Based on Boundary Distance Loss and Long-Tail Relation Enhancement 21st

Document-Level Relation Extraction Model Based on Boundary D...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Liu, Xin Yilahun, Hankiz Imam, Seyyare Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi830017 China School of National Security Studies Xinjiang University Urumqi830017 China

ISBN: (纸本)9789819601189

In today’s data-driven era, deriving meaningful insights from documents poses a considerable challenge. The task of Document-level Relation Extraction (DocRE) seeks to discern relationships among multiple entities within documents, mirroring real-world scenarios and introducing more complexity than sentence-level relation extraction (SLRE). DocRE encounters issues such as numerous widely dispersed entities, an imbalance between positive and negative instances, and a long-tail distribution of relation types. We introduce a model for document-level relation extraction that utilizes boundary distance loss and long-tail relation enhancement to tackle these challenges. This approach adeptly captures intricate interactions among entities, enhancing multi-relation extraction performance. Our model achieved improvements of 3.22% and 3.15% in F1 and Ign-F1 scores on the RE-DocRED dataset, respectively. Moreover, it significantly surpasses recent strong baseline models on the JacRED dataset. The experimental results confirm the model’s effectiveness and generalizability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Boundary distance adaptive threshold loss Document-level relation extraction Long-tail relationship enhancement Multi-relation extraction

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Multi-Relational Variational Contrastive Learning for Next POI Recommendation

Multi-Relational Variational Contrastive Learning for Next P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lin Chen Peipei Wang Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Next point-of-interest (POI) recommendation aims to predict the next interested POI to the user based on their historical check-in data in location-based social services. Most existing studies have attempted to model user visiting behaviors via sequence-based and graph-based models, and have achieved impressive performance. However, there is still room to explore the implicit transition preferences and contextual multiple semantic relationships among various POIs. To this end, we propose a novel graph-based Multi-Relational Variational Contrastive Learning (MRVCL) method for next POI recommendation, which captures local similarity associations and global contextual dependencies among POIs. Specifically, an order-free local-relational adaptive weighting module is designed to alleviate the problem of insufficient utilization of multi-hop neighbor information caused by the limit of neighbor order of nodes. We then develop a contextaware global-relational encoding module to capture implicit semantic sequential relationships. Finally, generative variational-contrastive learning is employed to construct a continuous representation of the latent features and reinforce the quality of representation learning. Extensive experiments on two real-world datasets validate that our MRVCL outperforms existing state-of-the-art methods on various evaluation metrics. To facilitate future research, our code and data are open-sourced at https://***/LinCH-en/MRVCL.

关键词： Representation learning Measurement Correlation Semantics Neural networks Contrastive learning Signal processing Transformers Encoding Speech processing

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A security-Enhanced Certificateless Anonymous Authentication Scheme with High Computational Efficiency for Mobile Edge Computing

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IEEE Transactions on network and Service Management 2025年

作者： Tian, Junfeng Wang, Yiting Shen, Yue Hebei University School of Cyberspace Security and Computer Key Laboratory on High Trusted Information System Baoding071000 China

Mobile Edge Computing (MEC), as a new computing paradigm, provides high-quality and low-latency services for mobile users and also reduces the load on cloud servers. However, MEC faces some security threats, such as data leakage, privacy leakage, and unauthorized access. To cope with these threats, many researchers have designed a series of identity-based authentication and key negotiation (ID-AKA) schemes for MEC environments. However, these schemes have some drawbacks, such as key escrow issues, lack of unlinkability and full anonymity, use of time-consuming bilinear pairing operations and insufficiently secure static public-private key pairs. To compensate for these drawbacks, we propose a certificateless anonymous authentication scheme for MEC with enhanced security and high computational efficiency. The scheme achieves unlinkability and full anonymity by using one-time pseudonyms generated by a tamper-proof device (TPD). The scheme also solves the key escrow problem and uses one-time public-private key pairs for authentication, thus enhancing the key security and communication security. In addition, the scheme eliminates bilinear pairing operations and precomputes some time-consuming operations in the TPD each time, thus optimizing the computational efficiency. Finally, we perform the security analysis and performance evaluation of the scheme. The results show that the scheme has the optimal computational efficiency and moderate communication costs, as well as significant advantages in terms of security, as compared to other competing schemes. © 2004-2012 IEEE.

关键词： Anonymity

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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Visible-Infrared Person Search: A Novel Benchmark and Solution 27th

Visible-Infrared Person Search: A Novel Benchmark and Solut...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Hongxu Xiong, Jianghao Huang, Yuheng Xie, Xiaohua Lai, Jian-Huang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Guangzhou GRGBanking Equipment Co. Ltd. Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9783031783401

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification (Re-ID). Existing person search methods and datasets predominantly focus on the visible light domain, and have difficulty in alleviating modality discrepancies. Furthermore, existing visible-infrared person Re-ID methods struggle to adequately address occlusions and handle background interference effectively. To address the above issues simultaneously, we first construct a new large-scale dataset, Multi-Modality Person Search (MMPS), which tackles the lack of suitable benchmarks for person search in the visible-infrared domain. Encompassing challenges of complex background interferences and occlusions under modality discrepancies, MMPS includes 21,470 images and 1,012 identities across six different cameras. Furthermore, we propose a novel visible-infrared person search method that integrates detection and Re-ID into a progressive process. Specifically, Progressive Inclusion (PI) is proposed to explore backgrounds and provide adaptive proposals. To better tackle the complex occlusions under significant modality discrepancies, we present Discriminative Mix (DM) to synthesize more diverse samples, leveraging specific pattern map embedding. This strategy ensures that our model is not overfitted to specific patterns and is capable of identifying diverse and distinctive human parts. Extensive experiments demonstrate that our method (PI-DM) achieves state-of-the-art performance on the task of visible-infrared person search. Our dataset has been released on https://***/sysuchx/MMPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Large datasets

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Underwater Image Restoration Based on Improved SwinIR Model 13

Underwater Image Restoration Based on Improved SwinIR Model

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13th International Conference on Intelligent Control and information Processing, ICICIP 2025

作者： Lu, Yanke Li, Fang Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

Image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinIR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed;while in the deep feature extraction phase, a Spatial Gated Feedforward network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands. © 2025 IEEE.

关键词： Convolution

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