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26th International Conference on information and Communications security, ICICS 2024

作者： Yang, Yang Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819788002

In this paper, we present a novel approach to construct code-based digital signature schemes. Our focus is on addressing the efficiency issues stemming from the low proportion of decodable syndromes in classical CFS signature scheme. To overcome this challenge, we introduce a well-defined syndrome subspace wherein all syndromes can be efficiently decoded. Additionally, our scheme differs from traditional code-based approaches by employing a novel method for constructing public and private keys, replacing permutation matrices with invertible matrices. This transformation yields a public key matrix that is statistically indistinguishable from a random matrix. We demonstrate that our scheme achieves existential unforgeability under an adaptive chosen message attack (EUF-CMA) in the random oracle model. Comparative analysis against other code-based signature schemes, such as Wave and Enhanced pqsigRM, reveals significant advantages in both public key and signature sizes. Furthermore, compared to the three post-quantum signature schemes (Crystals-Dilithium, Falcon, and Sphincs) selected as finalists by NIST PQC, our scheme still maintains a significant advantage in terms of signature size. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum cryptography

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Electronic band structures of topological kagome materials

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Chinese Physics B 2025年第1期34卷 29-44页

作者： Man Li Huan Ma Rui Lou Shancai Wang School of Information Network Security People’s Public Security University of ChinaBeijing 100038China Department of Physics Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)and Beijing Key Laboratory of Opto-electronic Functional Materials&Micronano DevicesRenmin University of ChinaBeijing 100872China Leibniz Institute for Solid State and Materials Research IFW Dresden01069 DresdenGermany Helmholtz-Zentrum Berlin fur Materialien und Energie Albert-Einstein-Strabe 1512489 BerlinGermany Joint Laboratory“Functional Quantum Materials”at BESSY II 12489 BerlinGermany

The kagome lattice has garnered significant attention due to its ability to host quantum spin Fermi liquid ***,the combination of unique lattice geometry,electron–electron correlations,and adjustable magnetism in solid kagome materials has led to the discovery of numerous fascinating quantum *** include unconventional superconductivity,charge and spin density waves(CDW/SDW),pair density waves(PDW),and Chern insulator *** emergent states are closely associated with the distinctive characteristics of the kagome lattice's electronic structure,such as van Hove singularities,Dirac fermions,and flat bands,which can exhibit exotic quasi-particle excitations under different symmetries and magnetic ***,various quantum kagome materials have been developed,typically consisting of kagome layers stacked along the z-axis with atoms either filling the geometric centers of the kagome lattice or embedded between the *** this topical review,we begin by introducing the fundamental properties of several kagome *** gain an in-depth understanding of the relationship between topology and correlation,we then discuss the complex phenomena observed in these *** include the simplest kagome metal T_(3)X,kagome intercalation metal T X,and the ternary compounds AT_(6)X_(6)and RT_(3)X_(5)(A=Li,Mg,Ca,or rare earth;T=V,Cr,Mn,Fe,Co,Ni;X=Sn,Ge;R=K,Rb,Cs).Finally,we provide a perspective on future experimental work in this field.

关键词： kagome lattice quasi-particle excitation electronic correlation magnetism

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Anomaly Detection in network Access-Using LSTM and Encoder-Enhanced Generative Adversarial networks

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 175-186页

作者： Hu, Jian He, Yingjun Xu, Wenqian Jiang, Yixin Liang, Zhihong Yang, Yiwei Yunnan Power Grid Information Center China Southern Power Grid Kunming650217 China Electric Power Research Institute China Southern Power Grid Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou510663 China

Along with the continuous development of information technology, the database has become an important module for enterprises and individuals to apply computers, and some important data are stored in the database, which also leads to the database becoming the target of malicious intruders. The abnormal access behavior detection algorithm for data can quickly identify abnormal access situations, timely intervention and processing to ensure data security. Based on this, this paper proposes an abnormal defense behavior detection algorithm based on generative adversarial network, the new algorithm has the applicability as well as two derivative models of generative adversarial network for network abnormal access detection with high efficiency. In this paper, we experiment the classification accuracy of network anomaly detection algorithm, i.e., F1, by using three models, namely, the original Generative Adversarial network (GAN), Generative Adversarial network using Long and Short-Term Memory network (GAN+LSTM), and Generative Adversarial network with the addition of an encoder (GAN+Encoder), and the result of this paper shows that the GAN+Encoder model is the most effective. And based on these three models of the generator and discriminator loss trends are compared, as well as through the iteration of 50 times of training results show that the GAN + Encoder model is relatively simple, and the training time is shorter and more efficient. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Generative adversarial networks

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Resource Allocation for MEC in Ultra-dense networks

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Journal of computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang 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 Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge Computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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Enhanced PIR Scheme Combining SimplePIR and Spiral: Achieving Higher Throughput Without Client Hints 6th

Enhanced PIR Scheme Combining SimplePIR and Spiral: Achievi...

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6th International Conference on Machine Learning for Cyber security, ML4CS 2024

作者： Xu, Haoyao Li, Yitong Tian, Haibo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819645657

In recent years, Private information Retrieval (PIR) schemes capable of rapid response have gotten significant attention, with SimplePIR, for instance, achieving a throughput of approximately 10 GB/s in a single-threaded setting. However, these schemes often require users to download data related to the database, known as client hints, making them unsuitable for scenarios where the database undergoes frequent updates. This paper, building upon the Spiral scheme, incorporates the precomputation approach of SimplePIR to design a query method that leverages simulatable homomorphic ciphertexts for offline precomputation. This enhancement not only improves the efficiency of the Spiral scheme but also addresses the issue of SimplePIR’s necessity for downloading client hints. Experimental results demonstrate that, on a database with 220 entries of 256 bytes each, the proposed scheme achieves a throughput 1.87 times higher than that of the original Spiral scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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Uncertainty-aware coarse-to-fine alignment for text-image person retrieval

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Deng, Yifei Chen, Zhengyu Li, Chenglong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei 230601 China

Text-to-image person retrieval, a fine-grained cross-modal retrieval problem, aims to search for person images from an image library that match a given textual caption. Existing text-to-image person retrieval methods usually use fixed-point embedding to express the semantics of the two modalities and perform multi-granularity alignment between modalities in the embedding space. However, owing to the inherent mutual one-to-many correspondence between images and texts, it is often difficult for fixed-point embedding methods to adequately capture this relationship, leading to erroneous retrieval results. To address this problem, we propose a novel uncertainty-aware coarse-to-fine alignment method, which first maps fixed-point embedding to probability distributions and then aligns two modalities in terms of distributions and sampling points at a coarse-to-fine granularity, for accurate text-to-image person retrieval. Specifically, we first introduce two contrastive learning tasks of distribution contrast learning and point contrast learning, to achieve coarse-grained inter-modal alignment with uncertainty-aware. The distribution contrast learning task ensures that distributions with the same identity are as similar as possible across modalities through distribution-based contrastive learning. The point contrast learning task performs the contrastive learning of inter-modal and intra-modal sampling points, which not only models rich and diverse cross-modal associations, but also optimizes the learning of distributions. For the fine-grained association requirements of text-to-image person retrieval, we design the task of uncertainty-aware attribute masking language reconstruction, which achieves fine-grained alignment by randomly masking attribute words in the text and reconstructing them via inter-modal sample point interactions. Extensive experiments on two public datasets demonstrate the superior performance of our method. © The Author(s) 2025.

关键词： Coarse-to-fine alignment Contrastive learning Cross-modal retrieval Probalility distribution Uncertainty-aware

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Multi-modal Self-perception Enhanced Large Language Model for 3D Region-of-Interest Captioning with Limited Data

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IEEE Transactions on Multimedia 2025年 27卷 2935-2948页

作者： Shi, Lu Kan, Shichao Jin, Yi Zhang, Linna Cen, Yigang Bejing Jiaotong University and the Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing100044 China Central South University School of Computer Science and Engineering Hunan Changsha0410083 China Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education and the School of Computer and lnformation Technology Beijing100044 China Guizhou University School of Mechanical Engineering Guiyang550025 China

3D Region-of-Interest (RoI) Captioning involves translating a model's understanding of specific objects within a complex 3D scene into descriptive captions. Recent advancements in Large Language Models (LLMs) have shown great potential in this area. Existing methods capture the visual information from RoIs as input tokens for LLMs. However, this approach may not provide enough detailed information for LLMs to generate accurate region-specific captions. In this paper, we introduce Self-RoI, a Large Language Model with multi-modal self-perception capabilities for 3D RoI captioning. To ensure LLMs receive more precise and sufficient information, Self-RoI incorporates Implicit Textual Info. Perception to construct a multi-modal vision-language information. This module utilizes a simple mapping network to generate textual information about basic properties of RoI from vision-following response of LLMs. This textual information is then integrated with the RoI's visual representation to form a comprehensive multi-modal instruction for LLMs. Given the limited availability of 3D RoI-captioning data, we propose a two-stage training strategy to optimize Self-RoI efficiently. In the first stage, we align 3D RoI vision and caption representations. In the second stage, we focus on 3D RoI vision-caption interaction, using a disparate contrastive embedding module to improve the reliability of the implicit textual information and employing language modeling loss to ensure accurate caption generation. Our experiments demonstrate that Self-RoI significantly outperforms previous 3D RoI captioning models. Moreover, the Implicit Textual Info. Perception can be integrated into other multi-modal LLMs for performance enhancement. We will make our code available for further research. © 1999-2012 IEEE.

关键词： Digital elevation model

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Deep anomaly detection on attributed networks by graph update

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Computing 2025年第6期107卷 1-25页

作者： Li, Yifan Li, Jiayin Liu, Ximeng Xu, Li College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou China College of Mathematics and Computer Science Fuzhou University Fujian Fuzhou China University Key Laboratory of Information Security of Network Systems Fuzhou University Fujian Fuzhou China

Anomaly detection on attributed networks has become extremely important as attributed networks are modeled in various real-world scenarios such as financial, communication, and social networks. Traditional anomaly detection methods such as degree-based or ego-network-based methods do not address well the complex interactions of attributes and structures in graph structures. Nowadays, graph autoencoder-based methods are a dominant graph anomaly detection method as the deep learning approach that better captures the complex interactions of graph data. However, the current approach ignores how to change the structure of the graph to improve the performance of graph anomaly detection leading to less than optimal anomaly detection results because there are many invalid or even obstructive edges in the original graph, which largely affect the results of graph anomaly detection. To address the above problems, we propose an applicable graph update method for graph anomaly detection, and we combine this method with that of a graph autoencoder to construct a new unsupervised graph anomaly detection framework to better utilize the updated graph structure. Specifically, we first make the anomalous nodes easier to distinguish by making similar nodes connected and removing unreasonable edges through graph updating. Then we use a graph autoencoder to reconstruct the error on the updated graph. Meanwhile the nodes with large errors we consider to be anomalous nodes. Particularly, when reconstructing the error, we reconstruct the original node attributes through the attributes of similar neighbors, which can better utilize the updated structure to improve the performance of anomaly detection. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all five real-world benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.

关键词： Unsupervised learning

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LayerCFL:an efcient federated learning with layer-wised clustering

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Cybersecurity 2025年第1期8卷 72-85页

作者： Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina Institute of Computer Science Faculty of Mathematics and Computer ScienceUniversity of GoettingenGöettingenGermany School of Information Technology Henan University of Chinese MedicineZhengzhouHenanChina

Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关键词： Federated learning Clustered federated learning Non-IID Layer-wised clustering

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