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Behavioral finance between the spot and futures markets based on multilayer network
Zhang Sicong, Dai Jianzhuo, Huang Wenjing, Mi Xinping
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 82-88.   DOI: 10.19682/j.cnki.1005-8885.2023.1013
Abstract886)      PDF(pc) (3117KB)(68)       Save
In order to study the financial behavior of investors in the spot market, the transmission process of futures prices to spot prices is analyzed. Firstly, a coarse-graining method is proposed to construct a dual-layer coupled complex network of spot price and futures price. Then, to characterize the financial behavior of investors in the spot market, a price coupling strength indicator is introduced to capture investors' overreaction and underreaction behavior. The simulation results show that, despite the focus of researchers on arbitrage opportunities between futures and spot markets, investors in the spot market will not overreact or delay when the acceptance level of price fluctuations remains unchanged. On the contrary, when the stable coefficient of the price difference between the futures and spot markets remains unchanged, investors undergo a nonlinear process of overreaction followed by underreaction as their acceptance level of price fluctuations increases.
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Stability and Hopf bifurcation analysis in DCTCP congestion control
Cheng Zunshui, Jiang Jingna, Sun Dongsheng
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 30-37.   DOI: 10.19682/j.cnki.1005-8885.2023.1015
Abstract675)      PDF(pc) (3657KB)(75)       Save
Traditional loss-based transports cannot meet the strict requirements of low latency and high throughput in data center networks (DCNs). Thus data center transmission control protocol (DCTCP) is proposed to better manage the congestion control in DCNs. To provide insight into improving the stability of the DCN, this paper focuses on the Hopf bifurcation analysis of a fluid model of DCTCP, and investigates the stability of the network. The round-trip time (RTT), being an effective congestion signal, is selected as the bifurcation parameter. And the network turns unstable and generates periodic solutions when the parameter is larger than the given critical value, which is given by explicit algorithms. The analytical results reveal the existence of Hopf bifurcation. Numerical simulations are performed to make a comparative analysis between the fluid model and the simplified model of DCTCP. The influence of other parameters on the DCN stability is also discussed.
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Dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems under directed weighted topology
Wu Yue, Chen Xiangyong, Qiu Jianlong, Hu Shunwei, Zhao Feng
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 3-10.   DOI: 10.19682/j.cnki.1005-8885.2023.1019
Abstract675)      PDF(pc) (1536KB)(224)       Save
This paper studies the dynamic event-triggered leader-follower consensus of nonlinear multi-agent systems (MASs) under directed weighted graph containing a directed spanning tree, and also considers the effects of disturbances and leader of non-zero control inputs in the system. Firstly, a novel distributed control protocol is designed for uncertain disturbances and leader of non-zero control inputs in MASs. Secondly, a novel dynamic event-triggered control ( DETC) strategy is proposed, which eliminates the need for continuous communication between agents and reduces communication resources between agents. By introducing dynamic thresholds, the complexity of excluding Zeno behavior within the system is reduced. Finally, the effectiveness of the proposed theory is validated through numerical simulation.
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Linear-quadratic optimal control for time-varying descriptor systems via space decompositions
Lv Pengchao, Huang Junjie, Liu Bo
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 38-48.   DOI: 10.19682/j.cnki.1005-8885.2023.1011
Abstract645)      PDF(pc) (1663KB)(72)       Save
This paper aims at solving the linear-quadratic optimal control problems ( LQOCP) for time-varying descriptor systems in a real Hilbert space. By using the Moore-Penrose inverse theory and space decomposition technique, the descriptor system can be rewritten as a new differential-algebraic equation (DAE), and then some novel sufficient conditions for the solvability of LQOCP are obtained. Especially, the methods proposed in this work are simpler and easier to verify and compute, and can solve LQOCP without the range inclusion condition. In addition, some  numerical examples are shown to verify the results obtained.
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RB-SLAM: visual SLAM based on rotated BEBLID feature point description
Fan Xinyue, Wu Kai, Chen Shuai
The Journal of China Universities of Posts and Telecommunications    2023, 30 (3): 1-13.   DOI: 10.19682/j.cnki.1005-8885.2023.1002
Abstract630)            Save
The extraction and description of image features are very important for visual simultaneous localization and mapping (V-SLAM). A rotated boosted efficient binary local image descriptor ( BEBLID) SLAM ( RB-SLAM) algorithm based on improved oriented fast and rotated brief (ORB) feature description is proposed in this paper, which can solve the problems of low localization accuracy and time efficiency of the current ORB-SLAM3 algorithm. Firstly, it uses the BEBLID to replace the feature point description algorithm of the original ORB to enhance the expressiveness and description efficiency of the image. Secondly, it adds rotational invariance to the BEBLID using the orientation information of the feature points. It also selects the rotationally stable bits in the BEBLID to further enhance the rotational invariance of the BEBLID. Finally, it retrains the binary visual dictionary based on the BEBLID to reduce the cumulative error of V-SLAM and improve the loading speed of the visual dictionary. Experiments show that the dictionary loading efficiency is improved by more than 10 times. The RB-SLAM algorithm improves the trajectory accuracy by 24.75% on the TUM dataset and 26.25% on the EuRoC dataset compared to the ORB-SLAM3 algorithm.
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Node interdependent percolation of multiplex hypergraph with weak interdependence
Zhang Junjie, Liu Caixia, Liu Shuxin, Zang Weifei, Li Qian
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 49-59.   DOI: 10.19682/j.cnki.1005-8885.2023.1016
Abstract612)      PDF(pc) (3049KB)(84)       Save
In recent years, there has been considerable attention and research on the higher-order interactions that are prevalent in various real-world networks. Hypergraphs, especially in the study of complex systems, are proved effective in capturing these interactions. To better characterize the model in reality, this paper proposes a theoretical model of node interdependent percolation in multiplex hypergraphs, considering “ weak ” interdependence. The proposed model includes pairwise and higher-order interactions, where the removal of nodes triggers cascading failures. However, interdependent nodes connected to failed nodes experience partial loss of connections due to “ weak” interdependence, reflecting the self-sustaining capabilities of real-world systems. Percolation theory is applied to the analysis to investigate the properties of the percolation threshold and phase transition. Both analysis and simulation results show that as the strength of interdependence between nodes weakens, the network transitions from a discontinuous to a continuous phase, thereby increasing its robustness.
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Fairness optimization and power allocation in cognitive NOMA / OMA V2V network with imperfect SIC
Liang Xiaolin, Liu Qianlong, Cao Wangbin, Liu Shuaiqi, Zhao Shuhuan, Zhao Xiongwen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 68-81.   DOI: 10.19682/j.cnki.1005-8885.2023.1020
Abstract601)      PDF(pc) (4592KB)(79)       Save
In order to improve the reliability and resource utilization efficiency of vehicle-to-vehicle (V2V) communication system, in this paper, the fairness optimization and power allocation for the cognitive V2V network that takes into account the realistic three-dimensional (3D) channel are investigated. Large-scale and small-scale fading are considered in the proposed channel model. An adaptive non-orthogonal multiple access ( NOMA) / orthogonal multiple access (OMA) scheme is proposed to reduce the complexity of successive-interference-cancellation (SIC) in decoding and improve spectrum utilization. Also, a fairness index that takes into account each user’s requirements is proposed to indicate the optimal point clearly. In the imperfect SIC, the optimization problem of maximizing user fairness is formulated. Then, a subgradient descent method is proposed to solve the optimization problem with customizable precision. And the computational complexity of the proposed method is analyzed. The achievable rate, outage probability and user fairness are analyzed. The results show that the proposed adaptive NOMA / OMA (A-NOMA / OMA) outperforms both NOMA and OMA. The simulation results are compared with validated analysis to confirm the theoretical analysis.
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Mainlobe interference suppression and beam pattern optimization methods
Du Xiaojuan, Tian Bin
The Journal of China Universities of Posts and Telecommunications    2023, 30 (2): 1-7.   DOI: 10.19682/j.cnki.1005-8885.2022.0024
Abstract575)      PDF(pc) (1575KB)(23)       Save

When the power of the mainlobe interference received by the receiver is at the same level as the power of the sidelobe interference power, the traditional eigen-projection interference suppression method has the problems of severe beam deformation and peak shift. Aiming at these problems, a beam pattern optimization method (BPOM) was proposed, which can suppress the interference well even when the mainlobe interference power is approximately equal to the sidelobe interference power. In the method, the mainlobe interference eigenvectors are firstly determined according to the correlation criterion. Then through the eigenvalue comparison, the sidelobe interference eigenvectors whose eigenvalues are approximately equal to the mainlobe interference eigenvalues are judged. After that, a projection matrix is constructed to filter out the mainlobe and sidelobe interference. Finally, the covariance matrix is reconstructed and the weight vector for beamforming is obtained. Simulation shows that BPOM has a better output performance than the existing algorithms in case that the power of the mainlobe interference is close to that of the sidelobe interference.

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Performance analysis of different coding schemes for wideband vehicle-to-vehicle MIMO systems
Liang Xiaolin, Rong Zhanyi, Cao Wangbin, Liu Shuaiqi, Zhao Xiongwen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 89-98.   DOI: 10.19682/j.cnki.1005-8885.2023.1017
Abstract574)      PDF(pc) (2637KB)(98)       Save
The signal is subjected to lots of interferences in vehicle-to-vehicle (V2V) channel propagation, resulting in receiving error codes. Two-dimensional (2D) and three-dimensional (3D) geometrical channel models are used to depict the wideband V2V multiple-input multiple-output (MIMO) channels. Using the channel model, Turbo code and low-density parity-check (LDPC) code are investigated for wideband V2V MIMO system, and the encoding and the decoding schemes are investigated. The bit error rate (BER), channel capacity and outage probability of wideband V2V MIMO system using Turbo code and LDPC code are analyzed at different typical speeds. The results show that the performance of wideband V2V MIMO system using Turbo code outperform that using LDPC code. The performance is affected by transmitting and receiving speeds with the same coding scheme. And the channel capacity of the 3D channel is larger than that of 2D channel.
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GNN-based temporal knowledge reasoning for UAV mission planning systems
Chai Rong, Duan Xiaofang, Wang Lixuan
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 12-25.   DOI: 10.19682/j.cnki.1005-8885.2024.2002
Abstract570)      PDF(pc) (2603KB)(50)       Save
Unmanned aerial vehicles (UAVs) are increasingly applied in various mission scenarios for their versatility, scalability and cost-effectiveness. In UAV mission planning systems (UMPSs), an efficient mission planning strategy is essential to meet the requirements of UAV missions. However, rapidly changing environments and unforeseen threats pose challenges to UMPSs, making efficient mission planning difficult. To address these challenges, knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs, missions, and environments. This paper investigates knowledge graph application in UMPSs, exploring its modeling, representation, and storage concepts and methodologies. Subsequently, the construction of a specialized knowledge graph for UMPS is detailed. Furthermore, the paper delves into knowledge reasoning within UMPSs, emphasizing its significance in timely updates in the dynamic environment. A graph neural network (GNN)-based approach is proposed for knowledge reasoning, leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge graph. For relation reasoning, path information is also incorporated to improve the accuracy of inference. To account for the temporal dynamics of the environment in UMPS, the influence of timestamps is captured through the attention mechanism. The effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.
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Parameter optimization of complex network based on the change-point identification
Xu Xingtao, Tao Jiagui
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 22-29.   DOI: 10.19682/j.cnki.1005-8885.2023.1014
Abstract567)      PDF(pc) (6155KB)(81)       Save
This paper proposes a novel method for the parameter optimization of complex networks established through coarsening and phase space reconstruction. Firstly, we identify the change-points of the time series based on the cumulative sum ( CUSUM) control chart method. Then, we optimize the coarse-graining parameters and phase space embedding dimension based on the evolution analysis of the global topology index ( betweenness) at the mutation point. Finally, we conduct a simulation analysis based on real-time data of Chinese copper spot prices. The results show that the delay of the copper spot prices in Chinese spot market is 1 day, and the optimal embedding dimension of the phase space reconstruction is 3. The acceptance level of the investors towards the small fluctuations in copper spot prices is 0.2 times than the average level of price fluctuations, which means that an average price fluctuation of 0.2 times is the optimal coarse- graining parameter.
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Distributed consensus of Lurie multi-agent systems under directed topology: a contraction approach
Zhang Xiaojiao, Wu Xiang
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 11-21.   DOI: 10.19682/j.cnki.1005-8885.2023.1018
Abstract566)      PDF(pc) (3622KB)(128)       Save
This paper is devoted to investigate the consensus problems for the multi-agent systems with Lurie nonlinear dynamics in directed topology. Under some assumptions, some sufficient conditions for the systems reaching leaderless consensus and tracking consensus are established by using contraction analysis theory. Compared with the existing results, there is no need to formulate the multi-agent networks in compact form. These conditions are only related to the individual agent in lower-dimensional case and the communication topology of the network. Additionally, a generalized nonlinear function is introduced. Finally, three numerical examples are demonstrated to illustrate the effectiveness of the theoretical results.
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Characteristics and modeling of UAV-vehicle MIMO wideband channels
Liang Xiaolin, Ma Jiaxu, Cao Wangbin, Xu Jianpeng, Liu Shuaiqi, Zhao Xiongwen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (6): 60-67.   DOI: 10.19682/j.cnki.1005-8885.2023.1012
Abstract524)      PDF(pc) (2101KB)(65)       Save
A geometry-based stochastic model ( GBSM) for unmanned aerial vehicle to vehicle ( UAV-V) multiple-input multiple-output (MIMO) wideband channel is proposed to investigate the characteristics of UAV-V channel. Based on the proposed model, a three-dimensional (3D) wideband channel matrix regarding channel numbers, time and delay is constructed. And some important channel characteristics parameters, such as power delay profile (PDP), root mean square ( RMS) delay spread, RMS Doppler spread, channel gain and Doppler power spectral density (PSD) are investigated with different vehicle velocities. It is much simpler and clearer compared with the complex analytical derivations. The results are compared with validated analysis to confirm the theoretical analysis.
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Design of high parallel CNN accelerator based on FPGA for AIoT
Lin Zhijian Gao Xuewei Chen Xiaopei Zhu Zhipeng Du Xiaoyong Chen Pingping
The Journal of China Universities of Posts and Telecommunications    2022, 29 (5): 1-9.   DOI: 10.19682/j.cnki.1005-8885.2022.0026
Abstract519)      PDF(pc) (3802KB)(64)       Save
To tackle the challenge of applying convolutional neural network (CNN) in field-programmable gate array (FPGA) due to its computational complexity, a high-performance CNN hardware accelerator based on Verilog hardware description language was designed, which utilizes a pipeline architecture with three parallel dimensions including input channels, output channels, and convolution kernels. Firstly, two multiply-and-accumulate (MAC) operations were packed into one digital signal processing (DSP) block of FPGA to double the computation rate of the CNN accelerator. Secondly, strategies of feature map block partitioning and special memory arrangement were proposed to optimize the total amount of off-chip access memory and reduce the pressure on FPGA bandwidth. Finally, an efficient computational array combining multiplicative-additive tree and Winograd fast convolution algorithm was designed to balance hardware resource consumption and computational performance. The high parallel CNN accelerator was deployed in ZU3EG of Alinx, using the YOLOv3-tiny algorithm as the test object. The average computing performance of the CNN accelerator is 127.5 giga operations per second (GOPS). The experimental results show that the hardware architecture effectively improves the computational power of CNN and provides better performance compared with other existing schemes in terms of power consumption and the efficiency of DSPs and block random access memory (BRAMs).
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SNR-adaptive deep joint source-channel coding scheme for imagesemantic transmission with convolutional block attention module
Yang Yujia, Liu Yiming, Zhang Wenjia, Zhang Zhi
The Journal of China Universities of Posts and Telecommunications    2024, 31 (1): 1-11.   DOI: 10.19682/j.cnki.1005-8885.2024.2001
Abstract500)      PDF(pc) (2443KB)(96)       Save
With the development of deep learning (DL), joint source-channel coding (JSCC) solutions for end-to-end transmission have gained a lot of attention. Adaptive deep JSCC schemes support dynamically adjusting the rate according to different channel conditions during transmission, enhancing robustness in dynamic wireless environment. However, most of the existing adaptive JSCC schemes only consider different channel conditions, ignoring the different feature importance in the image processing and transmission. The uniform compression of different features in the image may result in the compromise of critical image details, particularly in low signal-to-noise ratio (SNR) scenarios. To address the above issues, in this paper, a dual attention mechanism is introduced and an SNR-adaptive deep JSCC mechanism with a convolutional block attention module (CBAM) is proposed, in which matrix operations are applied to features in spatial and channel dimensions respectively. The proposedsolution concatenates the pooling feature with the SNR level and passes it sequentially through the channel attention network and spatial attention network to obtain the importance evaluation result. Experiments show that the proposed solution outperforms other baseline schemes in terms of peak SNR (PSNR) and structural similarity (SSIM), particularly in low SNR scenarios or when dealing with complex image content.
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Encrypted traffic classification based on fusion of vision transformer and temporal features
Wang Lanting, Hu Wei, Liu Jianyi Pang Jin, Gao Yating, Xue Jingyao, Zhang Jie
The Journal of China Universities of Posts and Telecommunications    2023, 30 (2): 73-82.   DOI: 10.19682/j.cnki.1005-8885.2023.0002
Abstract494)      PDF(pc) (1598KB)(28)       Save

Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network (CNN), recurrent neural network (RNN), and stacked autoencoder (SAE), and only construct a shallow network to extract features, which leads to the low accuracy of encrypted traffic classification, an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed. Bottleneck transformer network (BoTNet) was used to extract spatial features and bi-directional long short-term memory (BiLSTM) was used to extract temporal features. After the two sub-networks are parallelized, the feature fusion method of early fusion was used in the framework to perform feature fusion. Finally, the encrypted traffic was identified through the fused features. The experimental results show that the BiLSTM and BoTNet fusion transformer (BTFT) model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features. The accuracy rate of a virtual private network (VPN) and non-VPN binary classification is 99.9%, and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%.

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Performance analysis and low complexity receiver design for extra-large scale MIMO systems with residual hardware impairments
Lu Chang, Fang Yuan, Qiu Ling, Liang Xiaowen
The Journal of China Universities of Posts and Telecommunications    2023, 30 (2): 18-25.   DOI: 10. 19682/j.cnki.1005-8885.2023.0005
Abstract476)      PDF(pc) (1716KB)(17)       Save

The research purpose of this paper is focused on investigating the performance of extra-large scale massive multiple-input multiple-output ( XL-MIMO) systems with residual hardware impairments. The closed-form expression of the achievable rate under the match filter (MF) receiving strategy was derived and the influence of spatial non-stationarity and residual hardware impairments on the system performance was investigated. In order to maximize the signal-to-interference-plus-noise ratio ( SINR ) of the systems in the presence of hardware impairments, a hardware impairments-aware minimum mean squared error (HIA-MMSE) receiver was proposed. Furthermore, the stair Neumann series approximation was used to reduce the computational complexity of the HIA-MMSE receiver, which can avoid matrix inversion. Simulation results demonstrate the tightness of the derived

analytical expressions and the effectiveness of the low complexity HIA-MMSE (LC-HIA-MMSE) receiver.

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DRO-SLAM: Real-time object-aware SLAM for navigation robots and autonomous driving in dynamic environments
Wang Zixian, Zhang Miao, Yan Danfeng
The Journal of China Universities of Posts and Telecommunications    2023, 30 (3): 14-24.   DOI: 10.19682/j.cnki.1005-8885.2022.1011
Abstract470)            Save
Traditional simultaneous localization and mapping ( SLAM) mostly performs under the assumption of an ideal static environment, which is not suitable for dynamic environments in the real world. Dynamic real-time object-aware SLAM ( DRO-SLAM) is proposed in this paper, which is a visual SLAM that can realize simultaneous localizing and mapping and tracking of moving objects indoor and outdoor at the same time. It can use target recognition, oriented fast and rotated brief (ORB) feature points, and optical flow assistance to track multi-target dynamic objects and remove them during dense point cloud reconstruction while estimating their pose. By verifying the algorithm effect on the public dataset and comparing it with other methods, it can be obtained that the proposed algorithm has certain guarantees in real-time and accuracy, it also provides more functions. DRO-SLAM can provide the solution to automatic navigation which can realize lightweight deployment, provide more vehicles, pedestrians and other environmental information for navigation.
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Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification
Hong PENG yaozong liu
The Journal of China Universities of Posts and Telecommunications    2023, 30 (5): 1-10.   DOI: 10. 19682 / j. cnki. 1005-8885. 2023. 0010
Abstract470)      PDF(pc) (890KB)(214)       Save

    A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination (JGCFD-MDL) for image classification tasks was proposed. The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion. To further quantify the abstract features that characterize the data, a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance. Finally, the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty, and better performance is obtained. Experiments show that compared with the local similarity algorithm, the average precision is improved by 3.34% . Compared with the single-layer dictionary learning algorithm, the one-error is improved by 1.00% , and the average precision is improved by 0.54% . Experiments also show that it has better performance on general datasets.

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Dynamic multi-keyword fuzzy ranked search with leakage resilience over encrypted cloud data
The Journal of China Universities of Posts and Telecommunications    2023, 30 (2): 83-95.   DOI: 10.19682/j.cnki.10058885.2022.0023
Abstract462)      PDF(pc) (2118KB)(21)       Save

To achieve the confidentiality and retrievability of outsourced data simultaneously, a dynamic multi-keyword fuzzy ranked search scheme (DMFRS) with leakage resilience over encrypted cloud data based on two-level index structure was proposed. The first level index adopts inverted index and orthogonal list, combined with 2-gram and location-sensitive Hashing (LSH) to realize a fuzzy match. The second level index achieves user search permission decision and search result ranking by combining coordinate matching with term frequency-inverse document frequency (TF-IDF). A verification token is generated within the results to verify the search results, which prevents the potential malicious tampering by cloud service providers (CSP). The semantic security of DMFRS is proved by the defined leakage function, and the performance is evaluated based on simulation experiments. The analysis results demonstrate that DMFRS gains certain advantages in security and performance against similar schemes, and it meets the needs of storage and privacy-preserving for outsourcing sensitive data.

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