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Bidirectional Identity-Based Inner-Product Functional Re-Encryption in Vaccine data Sharing

IEEE Transactions on Cloud Computing 2025年

作者： Wang, Jing Zhou, Yanwei Zhu, Yasi Liu, Zhiquan Yang, Bo Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education China Jinan University College of Cyber Security Guangzhou510632 China Hubei University of Technology School of Computer Wuhan430068 China

With the development of cloud computing, more and more data is stored in cloud servers, which leads to an increasing degree of privacy of data stored in cloud servers. For example, in the critical domain of medical vaccine trials, where public health outcomes hinge on the analysis of sensitive patient data, the imperative to safeguard privacy has never been more pronounced. Traditional encryption methods, though effective at protecting data, often expose vulnerabilities during decryption and lack the ability to support granular data access and computation. One-way re-encryption schemes further impede the agility of data sharing, which is indispensable for the collaborative efforts of research institutions. To address these limitations, we propose a novel bidirectional re-encryption scheme for inner-product functional encryption (IPFE). Our scheme secures data while allowing computation and sharing in an encrypted state, preserving patient privacy without hindering research. By harnessing inner-product functional encryption, our approach allows authorized researchers to extract valuable insights from encrypted data, significantly enhancing privacy protections. Our scheme's security is predicated on the $l$-ABDHE (augmented bilinear Diffie-Hellman exponent) assumption, ensuring robustness against chosen plaintext attacks within the standard model. This foundation not only secures the data but also yields compact ciphertext length, minimizing storage demands. We introduce a protocol specifically designed for medical vaccine trials, which leverages our bidirectional IB-IPFRE (Identity-Based Inner-Product Functional Re-Encryption) scheme. This protocol enhances data security, supports collaborative research, and maintains patient privacy. Its application in vaccine trials demonstrates the scheme's effectiveness in protecting sensitive information while enabling critical research insights. © 2013 IEEE.

关键词： Bidirectional re-encryption CPA security identity-based encryption inner-product function encryption

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Probabilistic Electricity Price Forecasting and Uncertainty Estimation Using Deep Bayesian Model with Multi-Distribution Fusion

SSRN

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SSRN 2024年

作者： Shao, Zhen Han, Yating Fu, Chao Zha, Jianrui Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Hefei University of Technology Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Probabilistic electricity price forecasting (EPF) is paramount for stakeholder scheduling and trading in deregulated energy markets. However, during the process of establishing a probability prediction model, the consequential aleatoric and epistemic uncertainties are indistinguishable. Moreover, identifying epistemic uncertainty rather than aleatoric uncertainty is significant for conducting credible probabilistic EPF in cases with expected and unexpected fluctuations. This article proposes a hybrid approach under a Bayesian probability framework for performing reliable and accurate probabilistic forecasting. Specifically, it applies Bayesian deep learning model to convey the latent probability distribution of electricity prices, which represents the desirable epistemic uncertainty and inevitable aleatoric uncertainty. The embedded architecture employs a multi-head attention mechanism (MHAM) to allocate previous hidden states of long short-term memory (LSTM) cells, detecting both the local and global price patterns simultaneously. Considering the forecasting bias arising from aleatoric uncertainty in the process of establishing Bayesian networks, a new Wasserstein distance-based multi-distribution fusion mechanism driven by evidence theory is proposed and employed to identify and mitigate the aleatoric uncertainty of credible probabilistic EPF problems. The effectiveness of the proposed approach is evaluated on two real-world electricity price datasets and compared with twelve state-of-the-art models. © 2024, The Authors. All rights reserved.

关键词： Bayesian networks

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

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

作者： Shenhai Zheng Congyu Li Sian Wen Xi Gao Lei Yu College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L 2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP.

关键词： Training Adaptation models Federated learning Collaboration Signal processing Servers Speech processing Optimization Image classification Biomedical imaging

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Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC systems

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

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MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction

arXiv

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arXiv 2024年

作者： Qiao, Xiaoyu Li, Weisheng Wang, Guofen Huang, Yuping Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

Deep unfolding networks (DUNs) have demonstrated significant potential in accelerating magnetic resonance imaging (MRI). However, they often encounter high computational costs and slow convergence rates. Besides, they struggle to fully exploit the complementarity when incorporating multiple priors. In this study, we propose a multi-prior collaborative DUN, termed MCU-Net, to address these limitations. Our method features a parallel structure consisting of different optimization-inspired subnetworks based on low-rank and sparsity, respectively. We design a gates-controlled spatial attention module (GSAM), evaluating the relative confidence (RC) and overall confidence (OC) maps for intermediate reconstructions produced by different subnetworks. RC allocates greater weights to the image regions where each subnetwork excels, enabling precise element-wise collaboration. We design correction modules to enhance the effectiveness in regions where both subnetworks exhibit limited performance, as indicated by low OC values, thereby obviating the need for additional branches. The gate units within GSAMs are designed to preserve necessary information across multiple iterations, improving the accuracy of the learned confidence maps and enhancing robustness against accumulated errors. Experimental results on multiple datasets show significant improvements on PSNR and SSIM results with relatively low FLOPs compared to cutting-edge methods. Additionally, the proposed strategy can be conveniently applied to various DUN structures to enhance their performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Learning Multimodal Attention Mixed with Frequency Domain Information as Detector for Fake News Detection

Learning Multimodal Attention Mixed with Frequency Domain In...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zihan Ma Huan Liu Zhi Zeng Hao Guo Xiang Zhao Minnan Luo School of Computer Science and Technology Xi’an Jiaotong University Xi'an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi'an China Laboratory for Big Data and Decision Nation University of Defence Technology Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Detecting fake news on social media has become a crucial task in combating online misinformation and countering malicious propaganda. Existing methods rely on semantic consistency across modalities to fuse features and determine news authenticity. However, cunning fake news publisher manipulate image to ensure a high level of semantic consistency between news post and image, making it more difficult to distinguish fake news. To this end, we propose MHFFD (Mixed High-Frequency Feature Detector), a novel fake news detection framework that utilizes token-level semantic consistency evaluation to identify key elements in news content and provide guidance for discovering image manipulation and learning better news representations. Extensive experiments demonstrate that MHFFD outperforms state-of-the-art methods on two widely used fake news detection datasets. Further research also validates the effectiveness of token-level semantic alignment and manipulation detection.

关键词： Social networking (online) Fuses Frequency-domain analysis Semantics Detectors Feature extraction Fake news

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Learning Counterfactual Explanation of Graph Neural Networks Via Generative Flow Network

SSRN

引用

SSRN 2023年

作者： He, Kangjia Liu, Li Zhang, Youmin Wang, Ye Liu, Qun Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

Counterfactual subgraphs explain Graph Neural Networks (GNNs) by answering the question: "How would the prediction change if a certain subgraph were absent in the input instance?" The differentiable proxy adjacency matrix is prevalent in current counterfactual subgraph discovery studies due to its ability to avoid exhaustive edge searching. However, a prediction gap exists when feeding the proxy matrix with continuous values and the thresholded discrete adjacency matrix to GNNs, compromising the optimization of the subgraph generator. Furthermore, the end-to-end learning schema adopted in the subgraph generator limits the diversity of counterfactual subgraphs. To this end, we propose CF-GFNExplainer, a flow-based approach for learning counterfactual subgraphs. CF-GFNExplainer employs a policy network with a discrete edge removal schema to construct counterfactual subgraph generation trajectories. Additionally, we introduce a reward and loss function designed to guide CF-GFNExplainer's optimization. The discrete adjacency matrix generated in each trajectory eliminates the prediction gap, enhancing the validity of the learned subgraphs. Furthermore, the multi-trajectories sampling strategy adopted in CF-GFNExplainer results in diverse counterfactual subgraphs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of validity and diversity. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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SOT-MRAM-based true in-memory computing architecture for approximate multiplication

引用

Chip 2025年第2期4卷

作者： Song, Min Tang, Qilong Ouyang, Xintong Duan, Wei Xu, Yan Zhang, Shuai You, Long Key Laboratory of Intelligent Sensing System and Security of the Ministry of Education Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices School of Microelectronics Hubei University Wuhan430062 China School of Integrated Circuits Huazhong University of Science and Technology Wuhan430074 China

The in-memory computing (IMC) paradigm emerges as an effective solution to break the bottlenecks of conventional von Neumann architecture. In the current work, an approximate multiplier in spin-orbit torque magnetoresistive random access memory (SOT-MRAM) based true IMC (STIMC) architecture was presented, where computations were performed natively within the cell array instead of in peripheral circuits. Firstly, basic Boolean logic operations were realized by utilizing the feature of unipolar SOT device. Two majority gate-based imprecise compressors and an ultra-efficient approximate multiplier were then built to reduce the energy and latency. An optimized data mapping strategy facilitating bit-serial operations with an extensive degree of parallelism was also adopted. Finally, the performance enhancements by performing our approximate multiplier in image smoothing were demonstrated. Detailed simulation results show that the proposed 8 × 8 approximate multiplier could reduce the energy and latency at least by 74.2% and 44.4% compared with the existing designs. Moreover, the scheme could achieve improved peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), ensuring high-quality image processing outcomes. © 2025 The Author(s)

关键词： MRAM devices

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Improvement of the square-root low bounds on the minimum distances of BCH codes and Matrix-product codes

arXiv

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arXiv 2025年

作者： Wang, Xiaoqiang Li, Liuyi Wu, Yansheng Zheng, Dabin Lu, Shuxian Hubei Key Laboratory of Applied Mathematics Key Laboratory of Intelligent Sensing System and Security Ministry of Education Faculty of Mathematics and Statistics Hubei University Wuhan430062 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China

The task of constructing infinite families of self-dual codes with unbounded lengths and minimum distances exhibiting square-root lower bounds is extremely challenging, especially when it comes to cyclic codes. Recently, the first infinite family of Euclidean self-dual binary and nonbinary cyclic codes, whose minimum distances have a square-root lower bound and have a lower bound better than square-root lower bounds are constructed in [6] for the lengths of these codes being unbounded. Let q be a power of a prime number and Q = q2. In this paper, we first improve the lower bounds on the minimum distances of Euclidean and Hermitian duals of BCH codes with length (formula presented) over (formula presented) and (formula presented) over FQ in [11], [13], [31] for the designed distances in some ranges, respectively, where (formula presented). Then based on matrix-product construction and some lower bounds on the minimum distances of BCH codes and their duals, we obtain several classes of Euclidean and Hermitian self-dual codes, whose minimum distances have square-root lower bounds or a square-root-like lower bounds. Our lower bounds on the minimum distances of Euclidean and Hermitian self-dual cyclic codes improved many results in [6]. In addition, our lower bounds on the minimum distances of the duals of BCH codes are almost qs − 1 or q times that of the existing lower bounds. © 2025, CC BY.

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Few-Shot Camouflaged Object Segmentation

Few-Shot Camouflaged Object Segmentation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ziqiu Wang Yuying Li Yang Yang Yamin Li Gaoyang Liu Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the domain of computer vision, Camouflaged Object Segmentation (COS) is a crucial task aimed at identifying objects that blend into their surroundings, with applications spanning diverse sectors such as military, medical, and beyond. Traditional COS techniques, which primarily depend on supervised learning, necessitate large-scale labeled datasets. However, the acquisition of sufficient camouflage images for such purposes is often constrained due to their scarcity and the high cost of manual annotation. Additionally, these conventional methods frequently struggle to generalize to novel, unseen classes. In response to these challenges, this paper proposes the Camouflage Few-Shot (CAMFS) framework, an innovative approach integrating few-shot learning into COS. The CAMFS framework comprises two main components: the Camouflaged-Meta module, which converts the semantic information of camouflaged objects into compact feature vectors to facilitate knowledge transfer from support to query images; and the Camouflaged-Base module, focused on refining edge detection and enhancing the contrast between foreground and background elements. To overcome the limitations of existing COS datasets, which are primarily designed for supervised learning, we have developed the COS-FSS dataset, the first public few-shot COS dataset. It is based on the COD10K dataset and supplemented with approximately 3200 additional camouflage images. We conducted extensive evaluations of our CAMFS framework on the COS-FSS dataset. Compared to existing COS models, CAMFS demonstrates an average improvement of 5.7% in S α and 14.02% in $F_\beta ^w$, while against few-shot segmentation models, it achieves a 5.97% increase in m-IoU. The dataset and additional resources are available at https://***/CAM-FSS/FSS-COD.

关键词： Image segmentation Military computing Supervised learning Semantics Refining Neural networks Object segmentation

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