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Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

Chinese Journal of Electronics 2022年第4期31卷 741-751页

作者： KANG Haiyan JI Yuanrui ZHANG Shuxuan school of information Management Beijing Information Science and Technology University computer school Beijing Information Science and Technology University

With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling(DRS-S)algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy(PDP) algorithm and privacy preserving approach based on clustering and noise(PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.

关键词： enhanced privacy preserving privacy enhancement method DRS-S algorithm sampling methods personalized differential privacy algorithm Relational databases privacy protection requirements privacy budget data privacy Data security real data set clustering relational databases massive social network relational data social networking (online) sensitive privacy information personalized privacy protection Other topics in statistics information networks real data set noise pattern clustering social software dimensionality reduction segmentation sampling algorithm social network data protected data

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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

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Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu school of computer science and Technology Huaibei Normal University school of computer science and information Engineering Changzhou Institute of Technology school of computer science and information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： Component-based software Trustworthiness Weight User feedback Measurement

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A Hybrid Entropy and Blockchain Approach for Network Security Defense in SDN-Based IIoT

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Chinese Journal of Electronics 2023年第3期32卷 531-541页

作者： SU Jian JIANG Mengnan school of computer science Nanjing University of Information Science and Technology

In the industrial Internet of things(IIoT), various applications generate a large number of interactions and are vulnerable to various attacks, which are difficult to be monitored in a sophisticated way by traditional network architectures. Therefore, deploying software-defined network(SDN) in IIoT is essential to defend against various attacks. However, SDN has a drawback: there is a security problem of distributed denial-ofservice(DDoS) attacks at the control layer. This paper proposes an effective solution: DDoS detection within the domain using tri-entropy in information theory. The detected attacks are then uploaded to a smart contract in the blockchain, so that the attacks can be quickly cut off even if the same attack occurs in different domains. Experimental validation was conducted under different attack strengths and multiple identical attacks, and the results show that the method has better detection ability under different attack strengths and can quickly block the same attacks.

关键词： Software-defined network Hybrid entropy Blockchain Distributed denial-of-service attacks Industrial Internet of things

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Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

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Tsinghua science and Technology 2023年第3期28卷 421-432页

作者： Ying Chen Fengjun Zhao Yangguang Lu Xin Chen computer school Beijing Information Science and Technology UniversityBeijing 100101China

Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive services.The task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)devices.This study investigates task offloading scheduling in a dynamic MEC system.By integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply model.We jointly optimize local computing,offloading duration,and edge computing decisions to minimize system cost.On the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME.DTOME can make task offloading decisions by weighing system cost and queue stability.We quote dynamic programming theory to obtain the optimal task offloading strategy.Simulation results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.

关键词： mobile edge computing Internet of Things dynamic offloading hybrid energy supply stochastic optimization

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Adaptive estimation and control for uncertain nonlinear systems and full actuation control

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science China(information sciences) 2023年第11期66卷 169-184页

作者： Fei YAN Mingyuan ZHANG Guoxiang GU school of information science and Technology Southwest Jiaotong University school of Electrical Engineering and computer science Louisiana State University

We study adaptive control for a family of nonlinear systems, involving unknown and uncertain parameters. The proposed control law estimates the system parameters adaptively and stabilizes the closedloop system asymptotically for the initial state over any given bounded set of the state-space. Moreover,reconstruction filters are designed to obtain error residue signals and to enable the use of the least-squares algorithm for estimating the parameters, in order to achieve the convergence based on the persistent excitation condition and asymptotic linearization. The proposed methods are applicable to full actuation and under actuation control systems. Simulation studies are carried out for a pendulum system and for a third-order vehicle model, as well as control of vehicle platoons, validating the theoretical results presented in this paper.

关键词： adaptive control asymptotic stabilization full actuation and under actuation least-squares nonlinear systems

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YOLOv5ST:A Lightweight and Fast Scene Text Detector

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computers, Materials & Continua 2024年第4期79卷 909-926页

作者： Yiwei Liu Yingnan Zhao Yi Chen Zheng Hu Min Xia school of computer and science Nanjing University of Information Science and TechnologyNanjing210044China school of Automation Nanjing University of Information Science and TechnologyNanjing210044China

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

关键词： Scene text detection YOLOv5 lightweight object detection

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Small-signal stability and robustness analysis for microgrids under time-constrained DoS attacks and a mitigation adaptive secondary control method

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science China(information sciences) 2022年第6期65卷 107-124页

作者： Qiuye SUN Bingyu WANG Xiaomeng FENG Shiyan HU College of information science and Engineering Northeastern University school of Electronics and computer science University of Southampton

With the close integration of cyber and power systems, the consensus-based secondary frequency control in a microgrid is increasingly vulnerable to communication failures such as transmission delays and denial-of-service(DoS) attacks, which can affect the efficiency of frequency recovery in the secondary frequency control. Leveraging the small-signal model, this paper develops a novel cyber-physical system model to analyze the cross-layer effect of DoS attacks on microgrids. In this way, the cross-layer impact on the microgrid from the cyber system to the physical system can be convincingly analyzed. Based on the root approximation method, the tolerant saving time is designed for the microgrid as the index to evaluate the tolerance margin of the time-constrained DoS attack, and then the relationship between the margin and secondary control coefficients is found. A mitigation adaptive secondary control technique is proposed so that the attacked microgrid can dynamically tune the secondary control gain according to the saving time and tolerant saving time(TST). The simulation results show that although the microgrid with high secondary control gain has good dynamic robustness, its TST is low. In addition, the proposed adaptive secondary control system is significantly better than the traditional control system in terms of the stability performance of the microgrid under a DoS attack.

关键词： microgrid hierarchical control architecture cyber-physical system security denial-of-service attack adaptive secondary frequency control

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Reinforcement learning based energy efficient robot relay for unmanned aerial vehicles against smart jamming

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science China(information sciences) 2022年第1期65卷 234-246页

作者： Xiaozhen LU Jingfang JIE Zihan LIN Liang XIAO Jin LI Yanyong ZHANG Department of information and Communication Engineering Xiamen University school of computer science and Technology University of Science and Technology of China school of computer science and Educational Software Guangzhou University

Unmanned aerial vehicles(UAVs) with limited energy resources, severe path loss, and shadowing to the ground base stations are vulnerable to smart jammers that aim to degrade the UAV communication performance and exhaust the UAV energy. The UAV anti-jamming communication performance, such as the outage probability, degrades if the robot relay is not aware of the jamming policies and the UAV network topology. In this paper, we propose a robot relay scheme for UAVs against smart jamming, which combines reinforcement learning with a function approximation approach named tile coding, to jointly optimize the robot moving distance and relay power with the unknown jamming channel states and locations. The robot mobility and relay policy are chosen based on the received jamming power, the robot received signal quality,location and energy consumption, and the bit error rate of the UAV messages. We also present a deep reinforcement learning version for the robot with sufficient computing resources. It uses three deep neural networks to choose the robot mobility and relay policy with reduced sample complexity, so as to avoid exploring dangerous policies that lead to the high outage probability of the UAV messages. The network architecture of the three networks is designed with fully connected layers instead of convolutional layers to reduce the computational complexity, which is analyzed by theoretical analyses. We provide the performance bound of the proposed schemes in terms of the bit error rate, robot energy consumption and utility based on a game-theoretic study. Simulation results show that the performance of our proposed relay schemes,including the bit error rate, the outage probability, and the robot energy consumption outperforms the existing schemes.

关键词： unmanned aerial vehicles relay jamming game theory reinforcement learning

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Multi-attribute group decision-making method with linguistic q-rung orthopair fuzzy information based on bi-direction Choquet integral

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International Journal of Intelligent Computing and Cybernetics 2023年第2期16卷 341-363页

作者： Ling Weng Jian Lin Shujie Lv School of Computer and Information Science Fujian Agriculture and Forestry UniversityFuzhouChina

Purpose-The purpose of this paper is to develop the linguistic q-rung orthopair fuzzy set(LqROFS)information VIKOR method based on the bi-direction Choquet integral(BDCI),taking into account the correlation between information.The method can enrich the existing studies related to LqROFS information and better solve the problem of MAGDM problem.Design/methodology/approach-Since applying Choquet integral(CI)depict information interaction is a common operation in MAGDM.However,the traditional CI has some limitations.The unidirectional alignment may affect the MAGDM results.Therefore,a LqROFS-VIKOR method based on BDCI is proposed,where BDCI is used to aggregate the decision matrix.Furthermore,it is not reasonable to apply exact numbers to express the similarity between two qualitative data.Then a new method of defining similarity using linguistics is proposed.The similarity is used to calculate attribute weights.Findings-The validity and potential application of MAGMD method with linguistic q-rung orthopair fuzzy information based on BDCI are demonstrated in a numerical examples study.Originality/value-According to the study of available literature,the current research on LqROFS is incomplete.The existing studies of both similarity and aggregate operators have certain shortcomings.The definition of similarity proposed in this paper is more in line with reality.And compared with the existing methods,the BDCI-based aggregate operator can describe the interaction between information more reasonably.Based on this VIKOR method based on BDCI under the LqROFS environment can better select the alternative.

关键词： Linguistic q-rung orthopair fuzzy set Similarity measure Bi-direction choquet integral VIKOR method

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Community Detection Applications in Mobile Networks:Towards Data-Driven Design for Next Generation Cellular Networks

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Journal of Communications and information Networks 2023年第2期8卷 155-163页

作者： Ayman Gaber Nashwa Abdelbaki Tamer Arafa school of information Technology and computer science Nile UniversityGiza 12677Egypt

The existing radio access network(RAN)is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating cost.Innovation and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look like.The role of machine learning(ML)and artificial intelligence(AI)driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging problems.In this paper we surveyed RAN network base stations(BSs)clustering and its applications in the literature.The paper also demonstrates how to leverage community detection algorithms to understand underlying community structures within RAN.Tracking areas(TAs)novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of BSs into TA according to mobility patterns.Finally,live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering techniques.Results obtained showed that the new methodology saved up to 34.6%of inter TA signaling overhead and surpassing other conventional clustering algorithms.

关键词： RAN mobility optimization RAN intelligence community detection ML

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