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Secure resource allocation against colluding eavesdropping in a user-centric cell-free massive multiple-input multiple-output system

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Frontiers of Information Technology & Electronic engineering 2024年第4期25卷 500-512页

作者： Na LI Yuanyuan GAO Kui XU Xiaochen XIA Huazhi HU Yang LI Yueyue ZHANG college of communication engineering Army Engineering University of PLANanjing 210007China

We investigate the resource allocation problem of a cell-free massive multiple-input multiple-output system under the condition of colluding eavesdropping by multiple passive eavesdroppers.To address the problem of limited pilot resources,a scheme is proposed to allocate the pilot with the minimum pollution to users based on access point selection and optimize the pilot transmission power to improve the accuracy of channel estimation.Aiming at the secure transmission problem under a colluding eavesdropping environment by multiple passive eavesdroppers,based on the local partial zero-forcing precoding scheme,a transmission power optimization scheme is formulated to maximize the system’s minimum security spectral efficiency.Simulation results show that the proposed scheme can effectively reduce channel estimation error and improve system security.

关键词： Cell-free Colluding eavesdropping Limited pilot resources Access point selection Security rate

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Practical cloud storage auditing using serverless computing

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Science China(Information Sciences) 2024年第3期67卷 33-47页

作者： Fei CHEN Jianquan CAI Tao XIANG Xiaofeng LIAO college of computer Science and Software engineering Shenzhen University college of computer Science Chongqing University

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

关键词： cloud storage auditing serverless computing object storage usability

Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

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computer Modeling in engineering & Sciences 2024年第6期139卷 2673-2703页

作者： Yan Wang You Lu Yuqing Zhou Zhijian Zhao Department of computer and communication engineering Northeastern UniversityQinhuangdao066004China

Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.

关键词： Maximum correntropy criterion unscented Kalman filter inertial navigation system ultra-wideband bisecting kmeans clustering algorithm

Endogenous Security Formal Definition,Innovation Mechanisms,and Experiment Research in Industrial Internet

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Tsinghua Science and Technology 2024年第2期29卷 492-505页

作者： Hongsong Chen Xintong Han Yiying Zhang School of computer and communication engineering University of Science and Technology BeijingBeijing 100083China

With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more challenging.The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge.However,endogenous security lacks a scientific and formal definition in industrial Internet.Therefore,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis.Secondly,the endogenous security innovation mechanism is clearly defined.Thirdly,an improved clone selection algorithm based on federated learning is proposed.Then,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof.We conduct identity authentication experiments based on two types of blockchains and compare their experimental results.Based on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial *** of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge nodes.Finally,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.

关键词： industrial Internet endogenous security architecture federated learning blockchain

Localization for mixed near-field and far-field sources under impulsive noise

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Journal of Systems engineering and Electronics 2024年第2期35卷 302-315页

作者： GAO Hongyuan ZHANG Yuze DU Ya’nan CHENG Jianhua CHEN Menghan college of Information and communication engineering Harbin Engineering UniversityHarbin 150001China college of Intelligent Systems Science and engineering Harbin Engineering UniversityHarbin 150001China

In order to solve the problem that the performance of traditional localization methods for mixed near-field sources(NFSs)and far-field sources(FFSs)degrades under impulsive noise,a robust and novel localization method is proposed.After eliminating the impacts of impulsive noise by the weighted out-lier filter,the direction of arrivals(DOAs)of FFSs can be estimated by multiple signal classification(MUSIC)spectral peaks search.Based on the DOAs information of FFSs,the separation of mixed sources can be performed.Finally,the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors.The Cramer-Rao bounds(CRB)for the unbiased estimations of DOA and range under impulsive noise have been drawn.Simulation experiments verify that the proposed method has advantages in probability of successful estimation(PSE)and root mean square error(RMSE)compared with existing localization methods.It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio(GSNR),few snapshots,and strong impulse.

关键词： source localization far-field source(NFS) near-field source(FFS) impulsive noise Cramer-Rao bound(CRB)

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Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

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computers, Materials & Continua 2024年第1期78卷 1157-1170页

作者： Lujuan Deng Boyi Liu Zuhe Li School of computer and communication engineering Zhengzhou University of Light IndustryZhengzhou450002China

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data.Concate-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method.This fusion method does not utilize the correlation information between modalities.To solve this problem,this paper proposes amodel based on amulti-head attention mechanism.First,after preprocessing the original data.Then,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence.Next,the input coding sequence is fed into the transformer model for further processing and learning.At the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities.Finally,the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer.Through the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing tasks.Our model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

关键词： Emotion analysis deep learning cross-modal attention mechanism

Byzantine Robust Federated Learning Scheme Based on Backdoor Triggers

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computers, Materials & Continua 2024年第5期79卷 2813-2831页

作者： Zheng Yang Ke Gu Yiming Zuo School of computer and communication engineering Changsha University of Science and TechnologyChangsha410114China

Federated learning is widely used to solve the problem of data decentralization and can provide privacy protectionfor data owners. However, since multiple participants are required in federated learning, this allows attackers tocompromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciouslycreated local models to the server to affect the prediction performance and training speed of the global model. Todefend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoortriggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious localmodels can be identified by the server according to its validation dataset. Furthermore, we calculate the adjustmentfactors of local models according to the parameters of their final layers, which are used to defend against datapoisoning-based Byzantine attacks. To further enhance the robustness of our scheme, each localmodel is weightedand aggregated according to the number of times it is identified as malicious. Relevant experimental data showthat our scheme is effective against Byzantine attacks in both independent identically distributed (IID) and nonindependentidentically distributed (non-IID) scenarios.

关键词： Federated learning Byzantine attacks backdoor triggers

Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism

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computers, Materials & Continua 2024年第3期78卷 4071-4089页

作者： Lujuan Deng Ruochong Fu Zuhe Li Boyi Liu Mengze Xue Yuhao Cui School of computer and communication engineering Zhengzhou University of Light IndustryZhengzhou450002China

Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.

关键词： Multispectral pedestrian detection convolutional neural networks depth separable convolution spatially reweighted attention mechanism

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

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computers, Materials & Continua 2024年第3期78卷 4205-4223页

作者： Youlin Ji Bo Yin Ke Gu School of computer and communication engineering Changsha University of Science and TechnologyChangsha410114China

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

关键词： Security data sharing blockchain data query privacy protection