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A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

computers, Materials & Continua 2023年第4期75卷 373-391页

作者： Shujuan Tian Wenjian Ding Gang Liu Yuxia Sun Saiqin Long Jiang Zhu Xiangtan University Xiangtan411105China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105China Hunan International Scientific and Technological Cooperation Base of Intelligent Network Xiangtan411105China Hunan University Changsha410082China Jinan University Guangzhou510632China

In edge computing,a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair *** maximize such benefits,this paper proposes a dynamic multiattribute resource bidding mechanism(DMRBM).Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal *** isworth noting thatwhen edge providers and users trade with thirdparty agents which are not entirely reliable and trustworthy,their sensitive information is prone to be ***,the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction process,which is also very ***,this paper first adopts a privacy protection algorithm to prevent sensitive information from *** the premise that the sensitive data of both edge providers and users are protected,the prices of providers fluctuate within a certain ***,users can choose appropriate edge providers by the price-performance ratio(PPR)standard and the reward of lower price(LPR)standard according to their *** two standards can be evolved by two evaluation ***,this paper employs an approximate computing method to get an approximate solution of DMRBM in polynomial ***,this paper models the bidding process as a non-cooperative game and obtains the approximate optimal solution based on two standards according to the game *** the extensive experiments,this paper demonstrates that the DMRBM satisfies the individual rationality,budget balance,and privacy protection and it can also increase the task offloading rate and the system benefits.

关键词： Edge computing approximate computing nash equilibrium privacy protection

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Fine-Grained Software Vulnerability Detection via Neural Architecture Search 28th

Fine-Grained Software Vulnerability Detection via Neural Ar...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Du, Qianjin Kuang, Xiaohui Li, Xiang Zhao, Gang The Department of Computer Science and Technology Tsinghua University Beijing100084 China National Key Laboratory of Science and Technology on Information System Security Beijing100084 China

ISBN: (纸本)9783031306778

Vulnerability detection methods based on the deep learning have achieved remarkable performance improvements compared to traditional methods. Current deep learning-based detectors mostly use a single RNN or its variants (i.e., LSTM or GRU) to detect vulnerabilities. However, vulnerability detection is a multi-domain problem. Different types of vulnerabilities have different characteristics. Using a single neural network cannot perform well in all types of vulnerability detection tasks. Manually designing a matching neural network for each type of vulnerability detection task not only requires a lot of trials and computational resources but also highly relies on the knowledge and design experience of experts. To address this issue, in this paper, we propose a novel fine-grained vulnerability detection framework named A-DARTS, which is capable of searching for well-performed neural network architectures for different vulnerability detection tasks by introducing neural network architecture search (NAS) techniques. Specifically, we design a more efficient search space to ensure superior neural network architectures can be found by the search algorithm. Besides, we propose an adaptive differentiable search algorithm to search for superior neural network architectures. Experimental results show that searched models consistently outperform all baseline models and achieve significant performance improvements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Efficient privacy-preserving federated learning under dishonest-majority setting

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Science China(information Sciences) 2024年第5期67卷 323-324页

作者： Yinbin MIAO Da KUANG Xinghua LI Tao LENG Ximeng LIU Jianfeng MA School of Cyber Engineering Xidian University Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Key Laboratory of Information Security of Network Systems Fuzhou University

Federated learning(FL) is an emerging distributed learning paradigm that solves the problem of isolated data by jointly learning the global model through distributed ***, recent studies have shown that FL may not always guarantee sufficient privacy preservation, this is mainly because the model parameters(e.g., weights or gradients)may leak sensitive information to malicious adversaries.

关键词： learning setting dishonest efficient federated majority preserving privacy under

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DCCT-Net: A network Combined Dynamic CNN and Transformer for Image Compressive Sensing

DCCT-Net: A Network Combined Dynamic CNN and Transformer for...

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

作者： Lijuan Xu Haixiao Mei Fenghua Tong Dawei Zhao Fuqiang Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent end-to-end image compressive sensing networks primarily use Convolutional Neural networks (CNNs) and Transformers, each with distinct limitations: CNNs struggle with global feature capture, while Transformers lack local feature extraction. We propose a novel network, DCCT-Net, which combines Dynamic CNN (DCNN) and Transformer. This integration leverages DCNN’s local feature strengths and the Transformer’s global representation capabilities, resulting in superior image reconstruction quality. To further enhance the network’s performance, we propose a Feature Dynamic Augment Module (FDAM), which dynamically extracts features based on the saliency of segmented image regions, thereby amplifying the CNN’s local feature expression. Additionally, we design a Weighted Fusion Module (WFM), which optimizes the combination of local and global features extracted by the DCNN and Transformer, respectively. Extensive experiments demonstrate that our proposed DCCT-Net significantly outperforms most existing state-of-the-art methods in the field.

关键词： Image segmentation Image coding Signal processing Transformers Feature extraction Sensors Convolutional neural networks Speech processing Compressed sensing Image reconstruction

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IRS for The Unicast Realizable Rate Maximization in RSMA-ISAC

IRS for The Unicast Realizable Rate Maximization in RSMA-ISA...

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IEEE International Conference on Communications in China (ICCC)

作者： Bing Han Yang Gou Yinghui Ye Guangyue Lu Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Integrated Sensing and Communication (ISAC) has emerged as a promising technology for the next-generation wireless networks. Given that the rate splitting multiple access (RSMA) can maximize resource utilization and the intelligent reflecting surface (IRS) can intelligently control the wireless channel by adjusting the phase shift of the reflected components, this paper considers a IRS-aided RSMA-ISAC network and formulates a non-convex problem to maximize the unicast realizable rate by jointly optimizing the unicast and multicast transmit beamforming, the reflected beamforming and the common rate vector. To address the non-convexity, we decompose the problem into two sub-problems, both of which are solved by using auxiliary variables, successive convex approximation (SCA) and semidefinite relaxation (SDR) with sequential rank-one constraint relaxation (SROCR). Then the alternating optimization is proposed to obtain the optimal solution. Simulation results are presented to verify the effectiveness of the proposed algorithm and the superiority of the proposed resource allocation method in the perfect successive interference cancellation (SIC) scenario.

关键词： Wireless sensor networks Interference cancellation Unicast Array signal processing Simulation Wireless networks Integrated sensing and communication

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, Xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural networks

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Quantum Remote State Preparation Based on Quantum network Coding

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computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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An Adaptive Image Compression Algorithm Based on Deep Learning 23

An Adaptive Image Compression Algorithm Based on Deep Learni...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Yanxia Liang Meng Zhao Guangyue Lu Jing Jiang Hua He Tong Jia Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

In recent years, deep artificial neural networks have received wide attention because it surpasses the effect of traditional methods by parameter fitting under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity lead to necessity of image compression in communications. In this paper, we propose an adaptive image compression algorithm with variable compression rate and learning rate based on the neural network in deep learning. This algorithm can meet various requirements in different situations by adjusting the parameters reasonably. Experiment results show the feasibility, flexibility and effectiveness of our proposed algorithm. Lower errors are obtained compared to other algorithms under the same compression rate.

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