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FedGLP-ADP: Federated Learning with Gradient-Based Layer-Wise Personalization and Adaptive Differential Privacy 认领 引用
1
作者 Di Xiao Wenting Jiang Min Li 《Computers, Materials & Continua》 SCIE EI 2026年第6期2514-2530,共17页
The rapid advancement of the Internet of Things(IoT)has transformed edge devices from simple data collectors into intelligent units capable of local processing and collaborative learning.However,the vast amounts of se... The rapid advancement of the Internet of Things(IoT)has transformed edge devices from simple data collectors into intelligent units capable of local processing and collaborative learning.However,the vast amounts of sensitive data generated by these devices face severe constraints from“data silos”and risks of privacy breaches.Federated learning(FL),as a distributed collaborative paradigm that avoids sharing raw data,holds great promise in the IoT domain.Nevertheless,it remains vulnerable to gradient leakage threats.While traditional differential privacy(DP)techniques mitigate privacy risks,they often come at the cost of significantly reduced model performance—a limitation particularly pronounced in resource-constrained IoT environments characterised by non-independent and identically distributed(non-IID)data distribution.To bridge the gap between privacy preservation and high performance on heterogeneous data,this paper proposes a novel personalized federated learning(PFL)method,FedGLP-ADP.This method leverages historical gradient information to provide a more detailed partitioning of parameters,aiming to prevent personalized knowledge from being affected by noise as much as possible,thereby reducing model degradation.Building on this,we propose an adaptive DP mechanism that optimizes both the clipping and noising steps to minimize the impact of noise on global knowledge.Experimental results show that FedGLP-ADP exhibits superior performance compared to other representative methods under different privacy levels and non-IID degrees. 展开更多
关键词 Personalized federated learning non-IID data differential privacy parameter decoupling
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DPIL-Traj: Differential Privacy Trajectory Generation Framework with Imitation Learning 认领 引用
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作者 Huaxiong Liao Xiangxuan Zhong +4 位作者 Xueqi Chen Yirui Huang Yuwei Lin Jing Zhang Bruce Gu 《Computers, Materials & Continua》 SCIE EI 2026年第1期1530-1550,共21页
The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location re... The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns.However,the use of real-world trajectory data poses significant privacy risks,such as location reidentification and correlation attacks.To address these challenges,privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data.This paper introduces DPIL-Traj,an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation.Firstly,the framework incorporates Differential Privacy Clustering,which anonymizes trajectory data by applying differential privacy techniques that add noise,ensuring the protection of sensitive user information.Secondly,Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories.By learning from expert trajectories,this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories.Finally,Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns.Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%,and in terms of privacy performance by an average of 12.51%,compared to state-of-the-art approaches.Ablation studies further reveal that DP clustering effectively safeguards privacy,imitation learning enhances utility under noise,and the Markov module strengthens temporal coherence. 展开更多
关键词 Privacy-preserving trajectory generation differential privacy imitation learning Markov chain
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FedDPL:Federated Dynamic Prototype Learning for Privacy-Preserving Malware Analysis across Heterogeneous Clients 认领 引用
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作者 Danping Niu Yuan Ping +2 位作者 Chun Guo Xiaojun Wang Bin Hao 《Computers, Materials & Continua》 SCIE EI 2026年第3期1989-2014,共26页
With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these i... With the increasing complexity of malware attack techniques,traditional detection methods face significant challenges,such as privacy preservation,data heterogeneity,and lacking category information.To address these issues,we propose Federated Dynamic Prototype Learning(FedDPL)for malware classification by integrating Federated Learning with a specifically designed K-means.Under the Federated Learning framework,model training occurs locally without data sharing,effectively protecting user data privacy and preventing the leakage of sensitive information.Furthermore,to tackle the challenges of data heterogeneity and the lack of category information,FedDPL introduces a dynamic prototype learning mechanism,which adaptively adjusts the clustering prototypes in terms of position and number.Thus,the dependency on predefined category numbers in typical K-means and its variants can be significantly reduced,resulting in improved clustering performance.Theoretically,it provides a more accurate detection of malicious behavior.Experimental results confirm that FedDPL excels in handling malware classification tasks,demonstrating superior accuracy,robustness,and privacy protection. 展开更多
关键词 Malware classification data heterogeneity federated learning clustering differential privacy
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DP-Fed6G:An adaptive differential privacy-empowered federated learning framework for 6G networks 认领 引用
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作者 Miao Du Peng Yang +2 位作者 Yinqiu Liu Xiaoming He Mingkai Chen 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1994-2002,共9页
The advent of 6G networks is poised to drive a new era of intelligent,privacy-preserving distributed learning by leveraging advanced communication and AI-driven edge intelligence.Federated Learning(FL)has emerged as a... The advent of 6G networks is poised to drive a new era of intelligent,privacy-preserving distributed learning by leveraging advanced communication and AI-driven edge intelligence.Federated Learning(FL)has emerged as a promising paradigm to enable collaborative model training without exposing raw data.However,its deployment in 6G networks faces significant obstacles,including vulnerabilities to inference attacks,the complexities of heterogeneous and dynamic network environments,and the inherent trade-off between privacy protection and model performance.In response to these challenges,we introduce DP-Fed6G,a novel FL framework that integrates differential privacy(DP)to fortify data security while ensuring high-quality learning outcomes.Specifically,DPFed6G employs an adaptive noise injection strategy that dynamically adjusts privacy protection levels based on real-time 6G network conditions and device heterogeneity,ensuring robust data security while maximizing model performance and optimizing the trade-off between privacy and utility.Extensive experiments on three real-world healthcare datasets demonstrate that DP-Fed6G consistently outperforms existing baselines(DP-Fed SGD and DPFed Avg),achieving up to 10.3%higher test accuracy under the same privacy budget.The proposed framework thus provides a practical solution for secure and privacy-preserving AI in 6G,supporting intelligent decisionmaking in privacy-sensitive applications. 展开更多
关键词 Differential privacy Federated learning 6G Gaussian noise
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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A Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering 认领 引用
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作者 Fang Liu Xianghui Meng +1 位作者 Jiachen Li Sibo Guo 《Computers, Materials & Continua》 SCIE EI 2026年第2期632-652,共21页
With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods... With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods are deficient,failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points,leading to decreased data availability and privacy exposure problems.To address these problems,this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering(MLDP).The method first utilizes the DBSCAN clustering algorithm to classify location points into non-outliers and outliers.For non-outliers,the scoring function is designed by combining geographic information and semantic information,and the privacy budget is allocated according to the heat intensity of the hotspot area;for outliers,the scoring function is constructed to allocate the privacy budget based on their correlation with the hotspot area.By comprehensively considering the geographic information,semantic information,and correlation with hotspot areas of the location points,a reasonable privacy budget is assigned to each location point,andfinallynoise is added throughthe Laplacemechanismto realizeprivacyprotection.Experimental results on tworeal trajectory datasets,Geolife and T-Drive,show that the MLDP approach significantly improves data availability while effectively protecting location privacy.Compared with the comparison methods,the maximum available data ratio of MLDP is 1.Moreover,compared with the RandomNoise method,its execution time is 0.056–0.061 s longer,and the logRE is 0.12951–0.62194 lower;compared with KemeansDP,QTK-DP,DPK-F,IDP-SC,and DPK-Means-up methods,it saves 0.114–0.296 s in execution time,and the logRE is 0.01112–0.38283 lower. 展开更多
关键词 Location privacy protection DBSCAN clustering differential privacy hotspot area
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Security and privacy in edge computing:a survey of electric vehicles 认领 引用
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作者 Honghao Gao Wanqiu Huang +1 位作者 Yueshen Xu Youhuizi Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期223-235,共13页
Electric Vehicles(EVs)have developed into a complex ecosystem that includes many technical components such as task offloading on mobile devices,the Internet of Vehicles(IoV),and smart grids.Moreover,Edge Computing(EC)... Electric Vehicles(EVs)have developed into a complex ecosystem that includes many technical components such as task offloading on mobile devices,the Internet of Vehicles(IoV),and smart grids.Moreover,Edge Computing(EC)is a technique that relocates applications and services closer to end-users.This computing paradigm has been extensively adopted across many scenarios,effectively reducing the load on the cloud computing infrastructure and centralized server facilities.EVs are closely related to EC in many aspects since electric vehicles are typically supported by modern communication and Artificial Intelligence(AI)technologies,such as,sensor networks,computation offloading,autonomous systems,and blockchain.However,the diversity and heterogeneity of edge devices have raised many security and privacy concerns in electric vehicles,and some complex EC scenarios make addressing these issues even more challenging.In this paper,we provide a comprehensive review of the security and privacy concerns raised by EC in EVs.First,we elaborate on the development,characteristics,and applications of EC in EVs.Next,we describe the typical architectures used to ensure the security and privacy of EC in EVs.Then,we analyze the risks and challenges related to the security and privacy of EC in EVs,focusing on several significant scenarios(e.g.,offloading,the IoV,and smart grids).We also discuss current research progress on the security and privacy,covering methodologies,architectures,algorithms,insights,and performance.Finally,we discuss several future challenges and issues regarding the security and privacy of EC in EVs. 展开更多
关键词 Electric vehicle Edge computing Security and privacy Internet of vehicle Smart grid
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DP1180双相钢在高应变速率下的力学性能及断裂行为 认领 引用
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作者 董伊康 薛仁杰 +4 位作者 刘乐天 王学慧 赵楠 路洪洲 王立辉 《机械工程材料》 CAS CSCD 北大核心 2026年第1期58-64,共7页
对DP1180双相钢进行准静态(应变速率0.001,0.01s-1)和动态(应变速率0.1,1,10,50,100,200,500,1000 s-1)拉伸试验,研究了其力学特征、断裂行为以及微观结构演变规律。结果表明:随着应变速率增加,DP1180双相钢的抗拉强度、屈服强度... 对DP1180双相钢进行准静态(应变速率0.001,0.01s-1)和动态(应变速率0.1,1,10,50,100,200,500,1000 s-1)拉伸试验,研究了其力学特征、断裂行为以及微观结构演变规律。结果表明:随着应变速率增加,DP1180双相钢的抗拉强度、屈服强度、断后伸长率和均匀伸长率均增大,高应变速率(100~1000 s-1)下屈服强度的应变速率敏感性相比抗拉强度更大。随着应变速率增加,拉伸试样表面微观形貌由断层状变为撕裂状,横向微裂纹数量增多,但当应变速率达到1000 s-1时横向裂纹几乎消失,出现大量微孔洞型断裂形态。不同应变速率下DP1180双相钢均发生微孔聚集型韧性断裂,随着应变速率增加,断口处大尺寸韧窝数量增多,韧窝中心出现由马氏体破碎形成的较深孔洞。高应变速率拉伸后钢中的马氏体板条较细,位错密度较大。位错强化和马氏体变形是高应变速率下强度增大的主要原因;绝热温升激活马氏体的塑性变形能力则是塑性增大的主要原因。 展开更多
关键词 DP1180双相钢 应变速率 力学性能 断裂行为
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DP590钢表面处理对复合层板的层间结合性能优化分析 认领 引用
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作者 沈智 施进科 +3 位作者 安家菊 金康 郭平安 刘克明 《锻压技术》 CAS CSCD 北大核心 2026年第7期88-94,132,共7页
为研究DP590钢/玻璃纤维复合层板的层间结合性能,对DP590钢板进行了机械打磨、喷砂、电泳3种表面处理,通过表面组织观察和滚筒剥离试验相结合的方法,对表面处理工艺参数进行了对比研究。结果表明:表面组织的均匀性和有效接触面积是影响... 为研究DP590钢/玻璃纤维复合层板的层间结合性能,对DP590钢板进行了机械打磨、喷砂、电泳3种表面处理,通过表面组织观察和滚筒剥离试验相结合的方法,对表面处理工艺参数进行了对比研究。结果表明:表面组织的均匀性和有效接触面积是影响复合层板层间结合性能的两个主要因素,最终对比得出电泳处理20 min后的DP590钢板与玻璃纤维的结合性能最佳;在此参数下生产的复合层板的层间剥离强度达到214.2(N·mm)·mm-1;提出的表面处理工艺应用于汽车电池包上盖的生产中,满足验证要求,可以拓展纤维金属复合层板在汽车领域中的应用范围。 展开更多
关键词 DP590钢 玻璃纤维 复合层板 表面处理 表面组织 结合性能 剥离强度
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DP980高强度双相钢板韧性断裂建模及冲裁工艺模拟 认领 引用
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作者 韩国丰 丁子涵 +5 位作者 崔振祥 牛超 韩非 张凌宇 何霁 李淑慧 《锻压技术》 CAS CSCD 北大核心 2026年第6期154-164,共11页
为精准预测DP980高强度双相钢板在冲裁过程中的断裂行为,采用实验与有限元仿真相结合的方法,标定了其韧性断裂模型参数,并将该模型应用于冲裁工艺仿真。研究了一种基于应力不变量的断裂模型,通过设计并制备中心孔拉伸、缺口拉伸及面内... 为精准预测DP980高强度双相钢板在冲裁过程中的断裂行为,采用实验与有限元仿真相结合的方法,标定了其韧性断裂模型参数,并将该模型应用于冲裁工艺仿真。研究了一种基于应力不变量的断裂模型,通过设计并制备中心孔拉伸、缺口拉伸及面内剪切试样,结合数字图像相关(DIC)技术与有限元分析,标定了DP980钢在单向拉伸、平面应变及剪切等多种典型应力状态下的断裂应变及应力路径。材料的塑性行为采用Swift-Voce混合硬化模型进行描述,断裂模型参数则通过遗传算法进行优化。将最终标定的模型应用于DP980钢板的冲裁过程仿真,预测结果与实验结果吻合良好,验证了所构建断裂模型在模拟复杂冲裁工艺中的准确性。 展开更多
关键词 DP980高强钢 韧性断裂模型 冲裁工艺 数字图像相关技术 断裂应变
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FedPPB:基于PSO和Paillier加密算法的区块链联邦学习方法 认领 引用
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作者 沈凡凡 刘梓昂 +3 位作者 梁琦玮 徐超 陈勇 何炎祥 《计算机研究与发展》 EI CSCD 北大核心 2026年第3期685-709,共25页
在人工智能快速发展的背景下,数据隐私与系统效率成为分布式智能系统中的核心挑战。现有研究虽在一定程度上缓解了数据泄露问题,但在资源分配、系统开销和安全性方面仍存在显著瓶颈。为此,构建了一种基于粒子群优化(particle swarm opti... 在人工智能快速发展的背景下,数据隐私与系统效率成为分布式智能系统中的核心挑战。现有研究虽在一定程度上缓解了数据泄露问题,但在资源分配、系统开销和安全性方面仍存在显著瓶颈。为此,构建了一种基于粒子群优化(particle swarm optimization,PSO)和Paillier加密算法的区块链联邦学习方法,简称FedPPB,该方法融合PSO算法与Paillier同态加密算法,实现对系统角色的动态优化分配与训练参数的加密保护。首先,针对工作节点、验证节点和矿工节点的任务特点,通过PSO算法构建包含模型准确率、验证时间和区块生成时间的适应度函数,实现角色数量的动态调整;其次,工作节点通过Paillier算法对参数更新进行加密,验证节点解密参数并验证其合法性,矿工节点生成区块并更新全局模型;最后,从理论上证明了PSO算法和Paillier算法分别在角色分配和参数加密中的安全性。实验表明,在MNIST,Fashion-MNIST,CIFAR-10数据集上,当恶意节点的占比为15%和25%时,FedPPB显著优于现有方法,展现出更高的准确率与鲁棒性。 展开更多
关键词 联邦学习 区块链 隐私保护 Paillier 粒子群优化
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预变形对DP590双相钢性能的影响 认领 引用
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作者 张茜 牛星辉 +1 位作者 刘淑影 王嘉伟 《锻压技术》 CAS CSCD 北大核心 2026年第2期259-265,299,共7页
以DP590双相钢为研究对象,采用单轴拉伸试验对较大尺寸试样施加预变形量为0%、3%、6%、9%、12%和15%的6种预变形,卸载后采用线切割制样并准静态拉伸至失效,获取其力学性能、加工硬化行为和局部成形性能的演变规律。结果表明:随着预变形... 以DP590双相钢为研究对象,采用单轴拉伸试验对较大尺寸试样施加预变形量为0%、3%、6%、9%、12%和15%的6种预变形,卸载后采用线切割制样并准静态拉伸至失效,获取其力学性能、加工硬化行为和局部成形性能的演变规律。结果表明:随着预变形量的增加,DP590钢的强度升高,规定非比例延伸强度变化尤为明显,屈强比逐渐趋近于1,总伸长率轻微升高,且多道次成形更利于全局成形;预变形会改变材料的加工硬化行为,无预变形时DP590钢在小应变范围内加工硬化特性突出,而预变形后在大应变范围内加工硬化更显著,随着预变形量的增加,材料加工硬化能力降低;局部成形性能随着预变形量的增加呈线性降低趋势。针对拉延后翻边/整形零件,可将预变形量控制在6%~9%,以平衡后续型面特征成形和边缘局部抗裂需求。 展开更多
关键词 预变形 DP590双相钢 力学性能 加工硬化性能 局部成形性能
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DP600双相钢不同应变速率下的力学性能和本构模型 认领 引用
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作者 罗森侨 罗通强 张振伟 《锻压技术》 CAS CSCD 北大核心 2026年第7期248-255,共8页
针对DP600双相钢,探究了不同应变速率下材料的关键力学性能的演变规律;通过扫描电镜对比分析了不同应变速率下拉伸试样的断口微观形貌特征;选取Johnson-Cook模型、修正Johnson-Cook模型、Cowper-Symonds模型等对不同应变速率下材料的力... 针对DP600双相钢,探究了不同应变速率下材料的关键力学性能的演变规律;通过扫描电镜对比分析了不同应变速率下拉伸试样的断口微观形貌特征;选取Johnson-Cook模型、修正Johnson-Cook模型、Cowper-Symonds模型等对不同应变速率下材料的力学性能开展外延拟合,并与试验结果进行误差分析;基于帽形截面梁冲击试验与仿真对比分析,验证了3种模型的预测精度,获取了DP600双相钢的动态力学性能最优本构模型。结果可知:DP600双相钢表现出较强的应变速率正敏感性,且关键力学性能指标随应变速率的演化规律呈现出明显的差异性;随应变速率提升,试样的断口韧窝密度逐渐增大,尺寸则先减小后增加;采用修正Johnson-Cook模型表征动态力学性能的精度较高,拟合曲线关键参数的最大误差为2.53%,帽型截面梁冲击峰值载荷误差仅为0.8%、平均载荷误差为4.16%,可将此模型应用于整车碰撞仿真。 展开更多
关键词 DP600双相钢 本构模型 力学性能 断口形貌 落锤冲击
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DP1200高强钢激光焊接数值模拟与工艺研究 认领 引用
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作者 王艳杰 祖健豪 +2 位作者 杨海鑫 李源欣 李帅 《北华航天工业学院学报》 CAS 2026年第2期14-18,共5页
探讨激光焊接工艺参数对DP1200高强钢激光焊接接头性能的影响。通过有限元方法建立了三维数值模型,利用圆锥形热源模型对激光焊接过程进行模拟。采用光纤激光对1.2mm厚DP1200高强钢进行激光焊接,研究了从高到低不同热输入量(900J/cm~180... 探讨激光焊接工艺参数对DP1200高强钢激光焊接接头性能的影响。通过有限元方法建立了三维数值模型,利用圆锥形热源模型对激光焊接过程进行模拟。采用光纤激光对1.2mm厚DP1200高强钢进行激光焊接,研究了从高到低不同热输入量(900J/cm~180J/cm)对焊接接头低倍截面形貌、抗拉强度和硬度的影响。研究结果表明,热输入量对焊缝截面熔宽影响显著,熔宽随着焊接热输入量的降低而变窄,焊接接头未出现冷裂纹,有轻微咬边缺陷;随着热输入的减少,马氏体含量增多,焊缝硬度增加;焊接热影响区出现轻微的软化现象;模拟激光焊接接头与实际焊接结果吻合度达到90%,验证了模拟结果的准确性和可靠性;同时获得1.2mm厚DP1200高强钢激光焊接工艺窗口,即激光功率为1500W,热输入量为450J/cm~300J/cm时,焊接接头形貌和力学性能良好。 展开更多
关键词 激光焊接 DP1200钢 热输入量 数值模拟 力学性能
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HC340/590DP+Z高强度双相钢硬化模型研究 认领 引用
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作者 余建华 赵同铭 《锻压技术》 CAS CSCD 北大核心 2026年第5期307-314,共8页
高强度钢的硬化模型对成形仿真、碰撞性能预测及结构耐久性分析等具有重要影响,针对HC340/590DP+Z高强度双相钢的最优适用硬化模型开展分析。获取单向拉伸的真应力-真应变曲线,基于5种单一硬化模型和2种组合硬化模型开展参数拟合和精度... 高强度钢的硬化模型对成形仿真、碰撞性能预测及结构耐久性分析等具有重要影响,针对HC340/590DP+Z高强度双相钢的最优适用硬化模型开展分析。获取单向拉伸的真应力-真应变曲线,基于5种单一硬化模型和2种组合硬化模型开展参数拟合和精度分析;基于液压胀形试验获取大应变范围的有效应力-塑性应变曲线,验证各硬化模型在大应变范围的性能预测精度;选取3种高精度的硬化模型,对比杯突试验与有限元仿真的关键性能偏差,进一步验证模型的预测精度。结果表明:单向拉伸标定的模型决定系数较高,但各模型对大应变应力的预测趋势差异显著;大应变下,HC340/590DP+Z钢的强度呈递增趋势,液压胀形与单向拉伸曲线贴合度较高;Swift+Hockett-Sherby组合模型对大应变性能的预测精度最高,决定系数为0.9921,且杯突仿真与实测载荷-位移曲线高度一致,最大载荷和极限应变偏差分别为1.11%和0.29%,表明Swift+Hockett-Sherby组合模型可高精度地预测大应变HC340/590DP+Z钢的力学性能,并为相关工程分析提供参考。 展开更多
关键词 HC340/590DP+Z高强度双相钢 力学性能 硬化模型 液压胀形试验 杯突试验
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Security and Privacy Challenges,Solutions,and Performance Evaluation in AIoT-Enabled Smart Societies 认领 引用 被引量：1
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作者 Shahab Ali Khan Tehseen Mazhar +5 位作者 Syed Faisal Abbas Shah Wasim Ahmad Sunawar Khan Afsha BiBi Usama Shah Habib Hamam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期179-217,共39页
The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces ma... The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces major security and privacy concerns across domains such as healthcare,transportation,and smart cities.This Systemic Literature Review(SLR)addresses three research questions:identifying major threats and challenges in AIoT ecosystems,reviewing state-of-the-art security and privacy techniques,and evaluating their effectiveness.An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries,including IEEE Xplore,ACM Digital Library,ScienceDirect,SpringerLink,and Wiley Online Library,with a focus on security-and privacy-enhancing techniques such as blockchain,federated learning,and edge AI.The SLR identifies key challenges including data privacy leakage,authentication,cloud dependency,and attack surface expansion,and finds that emerging techniques,while promising,often involve trade-offs related to latency,scalability,and compliance.The study highlights future directions including lightweight cryptography,standardization,and explainable AI to support secure and trustworthy AIoT-enabled smart societies. 展开更多
关键词 Artificial Intelligence of Things(AIoT) smart societies security privacy blockchain federated learning edge computing
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Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks 认领 引用 被引量：1
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1214-1234,共21页
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h... In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services. 展开更多
关键词 6G networks ad hoc networks privacy scheduling algorithms diffusion models fuzzing algorithms
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SDP-FL:选择性差分隐私的工业物联网联邦学习框架 认领 引用 被引量：1
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作者 刘暄 刘亚 +2 位作者 王新中 赵逢禹 刘先蓓 《计算机应用研究》 CSCD 北大核心 2026年第3期720-728,共9页
随着工业物联网(IIoT)的快速发展,如何在保护数据隐私的前提下高效利用设备数据成为亟待解决的问题。联邦学习(FL)作为一种通过本地训练模型并共享模型参数的技术,已成为保障数据隐私的有效方法。然而,现有FL仍存在隐私泄露的风险。为此... 随着工业物联网(IIoT)的快速发展,如何在保护数据隐私的前提下高效利用设备数据成为亟待解决的问题。联邦学习(FL)作为一种通过本地训练模型并共享模型参数的技术,已成为保障数据隐私的有效方法。然而,现有FL仍存在隐私泄露的风险。为此,提出了一种面向工业物联网的选择性差分隐私联邦学习(SDP-FL)框架。该框架通过将智能工厂的终端设备作为客户端参与联邦学习,在客户端侧通过最小裁剪和高斯噪声保护局部模型隐私;在服务器端,采用基于损失函数差值的筛选机制来设定模型参数更新阈值,仅聚合高质量的本地模型。实验结果表明,SDP-FL框架在MNIST和CIFAR-10数据集上的分类准确率分别为97.8%和79.2%,较传统联邦学习方法分别提高了1.6和0.6个百分点。该方法有效避免了无用梯度的干扰,同时也提升了模型聚合效用。 展开更多
关键词 联邦学习 工业物联网 高斯差分隐私 最小裁剪
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亚麻E2F/DP转录因子基因家族鉴定及表达分析 认领 引用
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作者 刘萍萍 孙阎 +11 位作者 刘丹丹 唐立郦 杨洌 程莉莉 康庆华 宋喜霞 姜忠娟 刘烨 孙茹 吴广文 杨学 袁红梅 《江苏农业学报》 CSCD 北大核心 2026年第2期250-263,共14页
为明确亚麻E2F/DP家族基因的生物信息学特征及其在作物生长发育过程中的影响机制,本研究通过生物信息学方法,分析亚麻E2F/DP家族基因结构、顺式作用元件、染色体定位、物种内和物种间共线性特征,及其编码蛋白质的理化性质和互作网络,并... 为明确亚麻E2F/DP家族基因的生物信息学特征及其在作物生长发育过程中的影响机制,本研究通过生物信息学方法,分析亚麻E2F/DP家族基因结构、顺式作用元件、染色体定位、物种内和物种间共线性特征,及其编码蛋白质的理化性质和互作网络,并利用转录组测序(RNA-seq)技术分析不同激素处理下亚麻E2F/DP基因的表达模式。结果表明,亚麻基因组中共鉴定到14个E2F/DP家族基因,其中有7个属于E2F亚家族,3个属于DP亚家族,4个属于DEL亚家族;同一亚家族的E2F/DP基因结构及其编码蛋白质的保守基序分布、保守结构域高度相似。物种内共发现16对共线性基因,且存在共线性的两个基因位于不同的染色体上。亚麻E2F/DP家族基因启动子序列中含有丰富的光响应元件、激素响应元件、胁迫响应元件、发育过程特定元件等。3个DP亚家族蛋白质(Lus10014423、Lus10022620和Lus10023926)为关键枢纽蛋白。不同激素处理后,亚麻茎部中段、茎部下段与叶片中14个E2F/DP家族基因均呈现差异性表达,基于实时荧光定量PCR(qRT-PCR)得到的Lus10022620和Lus10039455基因相对表达量与转录组测序结果在变化趋势上基本一致。本研究结果为进一步开展激素对亚麻E2F/DP转录因子的调控机制研究提供了基础。 展开更多
关键词 亚麻 E2F/DP家族基因 转录因子
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华南飑线中闪电活动与ZDR和KDP柱的时空配置关系研究 认领 引用
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作者 翟慧妍 赵川鸿 +6 位作者 张义军 郑栋 徐良韬 姚雯 肖辉 杜赛 陈家福 《气象学报》 CAS CSCD 北大核心 2026年第3期593-608,共16页
基于广州S波段双偏振雷达和低频三维闪电探测系统,研究了2017年5月4和8日两次飑线过程中闪电活动与ZDR和KDP柱的时空配置关系。结果表明:(1)闪电活动初期(云闪频次低于50次/(6 min)、地闪频次低于10次/(6 min)),云闪和地闪活动主... 基于广州S波段双偏振雷达和低频三维闪电探测系统,研究了2017年5月4和8日两次飑线过程中闪电活动与ZDR和KDP柱的时空配置关系。结果表明:(1)闪电活动初期(云闪频次低于50次/(6 min)、地闪频次低于10次/(6 min)),云闪和地闪活动主要落在ZDR和KDP柱平面空间覆盖范围内,云闪更倾向于发生在KDP柱区域内(占比最大可超过40%)。随着闪电活动的活跃,云闪和地闪活动主要落在非ZDR和KDP柱平面覆盖范围。(2)ZDR和KDP柱体积变化可提前云闪和地闪活动趋势约30 min,相关系数可超过或接近0.9,且ZDR柱体积峰值可以较好地预示云闪和地闪峰值的出现。 展开更多
关键词 飑线 闪电活动 ZDR和KDP柱 时空配置
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