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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Zhuowei Shen Xiao Kou Taiyao Yang Haoqin Xu Dongbin Wang Shaobo Niu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Intergration Minster of Education Nanjing China School of Cyberspace Security Beijing University of Post and Telecommunications Beijing China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Certificateless public key cryptography (CL-PKC) has garnered significant attention in recent years due to its ability to address the complex certificate management requirements inherent in public key infrastructure (PKI) systems and the key escrow issue associated with identity-based cryptography (IBC). However, the efficient revocation of illegitimate users in authentication schemes based on CL-PKC remains a challenge. Moreover, certain schemes still exhibit security vulnerabilities, such as susceptibility to key replacement attacks, and also require performance enhancements. In this article, we propose a novel certificateless signature scheme that incorporates the RSA accumulator. By leveraging the witness in the RSA accumulator to produce the public keys of users, the authenticity of the public key can be efficiently verified, thereby eliminating key replacement attacks. Additionally, by removing specific elements from the RSA accumulator and broadcasting the update credential to legitimate users via a public channel, our proposed scheme allows the effective revocation of malicious users. security analysis and performance evaluations indicate that our scheme provides comprehensive security and achieves high performance in terms of computational and communication costs in both signingverification and revocation processes.

关键词： Performance evaluation Privacy Costs Identity-based encryption Authentication Broadcasting Forgery Encryption security Digital signatures

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Multiplex Graph Representation Learning with Homophily and Consistency 39

Multiplex Graph Representation Learning with Homophily and C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Yudi Nie, Ci He, Hongqing Mo, Yujie Zhu, Yonghua Wen, Guoqiu Zhu, Xiaofeng Guangxi Key Lab of Multisource Information Mining Security Guangxi Normal University Guilin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Information Systems Technology Design Pillar Singapore University of Technology and Design Singapore

ISBN: (纸本)157735897X

Although unsupervised multiplex graph representation learning (UMGRL) has been a hot research topic, existing UMGRL methods still has limitations to be addressed. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency. To address these issues, in this paper, we propose a new UMGRL method to explore both homophily and consistency in the multiplex graph. Specifically, we propose to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to improve the homophily and reduce the impact of the heterophily in the graph structure. We also design a contrastive loss based on a self-expression matrix of the node representation to achieve node-level and class-level consistency. Furthermore, we theoretically prove our method to achieve class-level consistency. Extensive experimental results on real datasets verify the effectiveness of the proposed method with respect to node classification tasks, compared to SOTA methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Unsupervised learning

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Structure Flow-Guided network for Real Depth Super-Resolution

arXiv

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arXiv 2023年

作者： Yuan, Jiayi Jiang, Haobo Li, Xiang Qian, Jianjun Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Real depth super-resolution (DSR), unlike synthetic settings, is a challenging task due to the structural distortion and the edge noise caused by the natural degradation in real-world low-resolution (LR) depth maps. These defeats result in significant structure inconsistency between the depth map and the RGB guidance, which potentially confuses the RGB-structure guidance and thereby degrades the DSR quality. In this paper, we propose a novel structure flow-guided DSR framework, where a cross-modality flow map is learned to guide the RGB-structure information transferring for precise depth upsampling. Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet). CFUNet contains a trilateral self-attention module combining both the geometric and semantic correlations for reliable cross-modality flow learning. Then, the learned flow maps are combined with the grid-sampling mechanism for coarse high-resolution (HR) depth prediction. PEANet targets at integrating the learned flow map as the edge attention into a pyramid network to hierarchically learn the edge-focused guidance feature for depth edge refinement. Extensive experiments on real and synthetic DSR datasets verify that our approach achieves excellent performance compared to state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model

arXiv

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arXiv 2024年

作者： Tang, Datao Cao, Xiangyong Hou, Xingsong Jiang, Zhongyuan Liu, Junmin Meng, Deyu The School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Information and Communications Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710049 China The School of Mathematics and Statistics the Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://***/Sonettoo/CRS-Diff. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A Federated Learning network Intrusion Detection System for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

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

作者： Yifan Zeng Peijia Zheng Jian Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning network Intrusion Detection System (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

关键词： Training Privacy Data privacy Accuracy Costs Federated learning network intrusion detection Signal processing Benchmark testing Speech processing

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Recurrent Structure Attention Guidance for Depth Super-Resolution

arXiv

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arXiv 2023年

Image guidance is an effective strategy for depth super-resolution. Generally, most existing methods employ handcrafted operators to decompose the high-frequency (HF) and low-frequency (LF) ingredients from low-resolution depth maps and guide the HF ingredients by directly concatenating them with image features. However, the hand-designed operators usually cause inferior HF maps (e.g., distorted or structurally missing) due to the diverse appearance of complex depth maps. Moreover, the direct concatenation often results in weak guidance because not all image features have a positive effect on the HF maps. In this paper, we develop a recurrent structure attention guided (RSAG) framework, consisting of two important parts. First, we introduce a deep contrastive network with multi-scale filters for adaptive frequency-domain separation, which adopts contrastive networks from large filters to small ones to calculate the pixel contrasts for adaptive high-quality HF predictions. Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery. In addition, we fuse the features of HF maps to enhance the edge structures in the decomposed LF maps. Extensive experiments show that our approach obtains superior performance compared with state-of-the-art depth super-resolution methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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New Identity Based Proxy Re-Encryption Scheme from Lattices

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China Communications 2019年第10期16卷 174-190页

作者： Liqiang Wu Xiaoyuan Yang Minqing Zhang Longfei Liu Key Lab of Network and Information Security Engineering University of Chinese Armed Police ForceXi’an 710086China Key Lab of Computer Network and Information Security Xi Dian UniversityXi’an 710071China

Identity-Based Proxy Re-Encryption (IB-PRE) allows a semi-trusted proxy to convert the ciphertext encrypted under Alice’s identity into Bob’s ciphertext of the same message without leaking plaintext. Lattice-based cryptography enjoys potential resistance to quantum analysis and low computational complexity. A multi-hop and unidirectional IB-PRE from lattices is presented. We split the functions of decryption and ciphertext transformation separately, and design the double private keys mechanism, where two keys are generated for each user, one key is used to decrypt the ciphertext by Pre-Image Sampling technique, and the other is used to generate the re-encryption key by Bonsai Trees technique. The generation of the re-encryption key is non-interactive and collusion resistant. Moreover, its IND-sID-CPA security over the decisional Learning With Errors (LWE) assumption under the standard model is proved. Compared with some previous IBPRE schemes from Bilinear Pairings, the format of transformed ciphertext in our scheme remains unchanged, furthermore, it can also resist quantum analysis. Compared with some existing IB-PRE schemes from lattices with similar properties, the space of the message in our scheme is a vector of length l and the encryption process remains a lower encryption blowup factor. At last, a proof-of-concept implementation is provided.

关键词： IB-PRE lattices bonsai trees collusion resistance

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Wireless Powered Backscatter Communications with Spatially Random Tags

Wireless Powered Backscatter Communications with Spatially R...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Yingting Ye, Yinghui Li, Xingwang Ma, Zhongyu Zhou, Zhiyang Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou730070 China State Grid Gansu Electric Power Research Institute Lanzhou730070 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China Xi'an University of Posts & Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

ISBN: (纸本)9798350345384

In this paper, a wireless powered backscatter communication (BackCom) system is considered, which consists of one dedicated radio-frequency source node (S), one destination node and multiple tags randomly located in the coverage region of S. The consumed power by tags is considered and an optimal dynamic reflection coefficient (RC) is used to maximize the power of the backscattered signals while just satisfying the tag's power requirement. The random tag selection (RTS) and nearest tag selection (NTS) schemes are proposed and the corresponding outage probabilities and diversity orders are derived. Numerical results verify the theoretical analysis and show that the NTS scheme has the best performance compared to the RTS and farthest tag selection schemes. Moreover, numerical results also show that the performance of the adopted optimal dynamic RC is superior to that of the fixed RC. © 2023 IEEE.

关键词： Backscattering

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On the MDS Condition of Generalized Expanded-Blaum-Roth Codes

On the MDS Condition of Generalized Expanded-Blaum-Roth Code...

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International Symposium on Problems of Redundancy in information and Control Systems (RED)

作者： Hanxu Hou Linqi Song Dongguan Key Lab of Artificial Information Network School of Electrical Engineering & Intelligentization Dongguan University of Technology Department of Computer Science City University of Hong Kong

Generalized Expanded-Blaum-Roth (GEBR) codes [You et al. 2020] are designed for large-scale distributed storage systems that have larger recoverability for single-symbol failures, multi-column failures and multi-row failures, compared with locally recoverable codes (LRC). GEBR codes encode an α × k information array into a pτ ×(k + r) array such that lines of slope i with 0 ≤ i ≤ r −1 have even parity and each column contains pτ−α local parity symbols, where p is an odd prime and k +r ≤ pτ. Necessary and sufficient conditions for GEBR codes to be (n, k) recoverable (i.e., any k out of n = k+r columns can retrieve all information symbols) are given in [Hou et al. 2023] for α = (p −1)τ. However, the (n, k) recoverable condition of GEBR codes is unknown when α < (p − 1)τ. Note that the (n, k) recoverable condition is reduced to be MDS condition if there is no local parity symbol in each column. In this paper, we present the (n, k) recoverable condition for GEBR codes for α < (p − 1)τ. Moreover, we characterize the necessary and sufficient (n, k) recoverable condition for GEBR codes when p is a special prime.

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