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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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A Linked List Encryption Scheme for Image Steganography without Embedding

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computer Modeling in engineering & Sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

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Frontiers of computer Science 2023年第4期17卷 127-139页

作者： Jian AN Siyuan WU Xiaolin GUI Xin HE Xuejun ZHANG School of Computer Science and Technology Shaanxi Province Key Laboratory of Computer NetworkXi’an Jiaotong UniversityXi’an 710049China School of Software Henan UniversityKaifeng 475001China School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive *** the limitation of calculation power,edge computing is introduced to release unnecessary data *** edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data *** involved in one task are required to cooperate to process the task *** privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and *** crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is *** mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality ***,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this ***(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in ***,the final result is calculated without revealing the privacy of *** on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.

关键词： crowdsensing edge computing devices blockchain quality assessment reinforcement learning

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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EG-Four: An Embedded GPU-Based Efficient ECC Cryptography Accelerator for Edge Computing

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IEEE Transactions on Industrial Informatics 2023年第6期19卷 7291-7300页

作者： Dong, Jiankuo Zhang, Pinchang Sun, Kaisheng Xiao, Fu Zheng, Fangyu Lin, Jingqiang Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information Engineering Beijing100007 China University of Science and Technology of China School of Cyber Security Hefei123456 China

With the continuous development of Industry 4.0 technology, the embedded devices in Industrial Internet of Things (IIoT) are showing explosive growth, and large-scale cyber attacks or related security incidents continue to sound the alarm bell of information security. IIoT has strict requirements on computing performance and energy consumption, which poses severe challenges to cryptographic algorithms, especially public key cryptographic algorithms with high computational complexity. Embedded graphic processing unit (GPU) devices, always as edge computing nodes or AI accelerators, are widely deployed in IIoT applications. In this article, we propose an embedded GPU-based Four Q (EG-Four Q) elliptic curve public key cryptographic acceleration scheme. As far as we know, EG-Four Q is the first work to completely implement Four Q on the GPU platforms, including finite field operations, point arithmetic, and scalar multiplication. Relying only on 36-W power consumption, our scalar multiplication performance reaches 1717 kops/s with the latency of 2.38 ms. In terms of the energy-efficiency ratio, EG-Four Q has significant advantages over other platforms such as advanced RISC machines (ARM) CPU, Intel CPU, field programmable gate array (FPGA), and desktop GPUs. The throughput of EG-Four Q is 1.75 times that of the fastest elliptic curve cryptography implementation based on the same platform and even exceeds the performance of Intel top server CPU E5-2699v3 (18-core). Based on the embedded GPU Xavier, EG-Four Q can act as a cryptographic edge computing module or even a cloud cryptographic accelerator, providing more efficient elliptic curve cryptographic services for IIoT. © 2005-2012 IEEE.

关键词： Graphics processing unit

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MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive L...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Huiting Zhang, Weiyu Shang, Yong Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (纸本)9798350386226

Drug-target binding affinity prediction is critical to drug design. Some existing computational methods rely on single-scale data and cannot fully integrate the rich information of multi-scale data, such as molecular structures and network information. In this paper, we propose the MBC-DTA model, which effectively integrates multi-scale data, and models the complex interactions between atoms and amino acids. We introduce GCN and GAT, which effectively extract the feature representation of molecular structure scale and network scale. Afterwards, the features obtained from the molecular structure scale are fed into a bilinear attention network, which captures the intricate interaction information between drug-target pairs. Additionally, a cross-scale graph contrastive learning strategy is applied, optimizing the feature representations across different scales. Extensive experimental results on two benchmark datasets demonstrate that the MBC-DTA model outperforms existing state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

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computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Gang Chen, Tianjiao Li, Min Han, Delong Zhou, MingLe Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781665410205

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P3M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods. © 2024 IEEE.

关键词： Anomaly detection

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