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20 April 2024, Volume 51 Issue 2
  • Research on the multi-objective algorithm of UAV cluster task allocation
    GAO Weifeng, WANG Qiong, LI Hong, XIE Jin, GONG Ma...
    2024, 51(2):  1-12.  doi:10.19665/j.issn1001-2400.20230413
    Abstract ( 167 )   HTML( 104 )   PDF (2779KB) ( 104 )   Save

    Aiming at the cooperative task allocation problem of UAV swarm in target recognition scenario,an optimization model with recognition cost and recognition benefit as the goal is established,and a multi-objective differential evolution algorithm based on decomposition is designed to solve the model.First,an elite initialization method is proposed,and the initial solution is screened to improve the quality of the solution set on the basis of ensuring the uniform distribution of the obtained nondominated solution.Second,the multi-objective differential evolution operator under integer encoding is constructed based on the model characteristics to improve the convergence speed of the algorithm.Finally,a tabul search strategy with restrictions is designed,so that the algorithm has the ability to jump out of the local optimal.The algorithm provides a set of nondominated solution sets for the solution of the problem,so that a more reasonable optimal solution can be selected according to actual needs.After obtaining the allocation scheme by the above method,the task reallocation strategy is designed based on the auction algorithm,and the allocation scheme is further adjusted to cope with the unexpected situation of UAV damage.On the one hand,simulation experiments verify the effectiveness of the proposed algorithm in solving small,medium and large-scale task allocation problems,and on the other hand,compared with other algorithms,the nondominated set obtained by the proposed algorithm has a higher quality,which can consume less recognition cost and obtain higher recognition revenue,indicating that the proposed algorithm has certain advantages.

    Workflow deployment method based on graph segmentation with communication and computation jointly optimized
    MA Yinghong, LIN Liwan, JIAO Yi, LI Qinyao
    2024, 51(2):  13-27.  doi:10.19665/j.issn1001-2400.20231206
    Abstract ( 52 )   HTML( 46 )   PDF (3074KB) ( 46 )   Save

    For the purpose of improving computing efficiency,it becomes an important way for cloud data centers to deal with the continuous growth of computing and network tasks by decomposes complex large-scale tasks into simple tasks and modeling them into workflows,which are then completed by parallel distributed computing clusters.However,the communication bandwidth consumption caused by inter-task transmission can easily cause network congestion in data center.It is of great significance to deploy workflow scientifically,taking into account both computing efficiency and communication overhead.There are two typical types of workflow deployment algorithms:list-based workflow deployment algorithm and cluster-based workflow deployment algorithm.However,the former focuses on improving the computing efficiency while does not pay attention to the inter-task communication cost,so the deployment of large-scale workflow is easy to bring heavy network load.The latter focuses on minimizing the communication cost,but sacrifices the parallel computing efficiency of the tasks in the workflow,which results in a long workflow completion time.This work fully explores the dependency and parallelism between tasks in workflow,from the perspective of graph theory.By improving the classic graph segmentation algorithm,community discovery algorithm,the balance between minimizing communication cost and maximizing computation parallelism was achieved in the process of workflow task partitioning.Simulation results show that,under different workflow scales,the proposed algorithm reduces the communication cost by 35%~50%,compared with the typical list-based deployment algorithm,and the workflow completion time by 50%~65%,compared with the typical cluster-based deployment algorithm.Moreover,its performance has good stability for workflows with different communication-calculation ratios.

    UAV swarm power allocation strategy for resilient topology construction
    HU Jialin, REN Zhiyuan, LIU Anni, CHENG Wenchi, LI...
    2024, 51(2):  28-45.  doi:10.19665/j.issn1001-2400.20230314
    Abstract ( 51 )   HTML( 32 )   PDF (5173KB) ( 32 )   Save

    A topology construction method of the Unmanned combat network with strong toughness is proposed for the problem of network performance degradation and network paralysis caused by the failure of the Unmanned combat network itself or interference by enemy attack.The method first takes the edge-connectivity as the toughness indicator of the network;second,the minimum cut is used as the measure of the toughness indicator based on the maximum flow minimum cut(Max-flow min-cut) theorem,on the basis of which considering the limited power of a single UAV and the system,the topology is constructed by means of power allocation to improve the network toughness from the physical layer perspective,and the power allocation strategy of the Unmanned combat network under power constraint is proposed;finally,particle swarm optimization(PSO) algorithm is used to solve the topology toughness optimization problem under the power constraint.Simulation results show that under the same modulation and power constraints,the power allocation scheme based on the PSO algorithm can effectively improve the toughness of the Unmanned combat network compared with other power allocation algorithms in the face of link failure mode and node failure mode,and that the average successful service arrival rate of the constructed network remains above 95% in about 66.7% of link failures,which meets the actual combat requirements.

    Optimization of light sources for the IRS-assisted indoor VLC system considering HPSA
    HE Huimeng, YANG Ting, SHI Huili, WANG Ping, BING ...
    2024, 51(2):  46-55.  doi:10.19665/j.issn1001-2400.20240103
    Abstract ( 27 )   HTML( 22 )   PDF (2522KB) ( 22 )   Save

    Aiming at the problem of unevenness of optical power distribution on the receiving plane in a visible light communication(VLC) system,a light source optimization method for an intelligent reflecting surface(IRS)-assisted indoor VLC system based on the hybrid particle swarm algorithm(HPSA) is proposed.Taking the two layout schemes of rectangular and hybrid arrangements with 16 light-emitting diodes(LEDs) as examples,the variance of received optical power on the receiving plane is set as the fitness function,and the proposed HPSA is combined with the IRS technology to optimize the half-power angle and positional layout of LEDs as well as the yaw and roll angles of IRS.Subsequently,initial(unoptimized) optimization using the HPSA,and optimization using the HPSA for the IRS-aided VLC systems are simulated and compared.The results indicate that when considering the first reflection link,compared to the original VLC system,the fluctuations of received optical power and signal-to-noise ratio of the VLC system optimized with the HPSA significantly decrease for both light source layouts;the HPSA optimized IRS-aided indoor VLC system improves the received optical power fluctuations in the rectangular layout as well as the HPSA optimized VLC system,and its performance is significantly better than that of the HPSA optimized VLC system only in the hybrid layout for optical power fluctuations improvement.Among the three VLC systems,the IRS-aided VLC system based on HPSA optimization has the largest average received optical power.Besides,the average root mean square delay spread performance of the above three VLC systems using a hybrid layout is better than that of a rectangular layout.This work will benefit the study of light source distribution in indoor VLC systems.

    Highly dynamic multi-channel TDMA scheduling algorithm for the UAV ad hoc network in post-disaster
    SUN Yanjing, LI Lin, WANG Bowen, LI Song
    2024, 51(2):  56-67.  doi:10.19665/j.issn1001-2400.20230414
    Abstract ( 33 )   HTML( 23 )   PDF (1608KB) ( 23 )   Save

    Extreme emergencies,mainly natural disasters and accidents,have posed serious challenges to the rapid reorganization of the emergency communication network and the real-time transmission of disaster information.It is urgent to build an emergency communication network with rapid response capabilities and dynamic adjustment on demand.In order to realize real-time transmission of disaster information under the extreme conditions of "three interruptions" of power failure,circuit interruption and network connection,the Flying Ad Hoc Network can be formed by many unmanned aerial vehicles to cover the network communication in the disaster-stricken area.Aiming at the channel collision problem caused by unreasonable scheduling of FANET communication resources under the limited conditions of complex environment after disasters,this paper proposes a multi-channel time devision multiple access(TDMA) scheduling algorithm based on adaptive Q-learning.According to the link interference relationship between UAVs,the vertex interference graph is established,and combined with the graph coloring theory,and the multi-channel TDMA scheduling problem is abstracted into a dynamic double coloring problem in highly dynamic scenarios.Considering the high-speed mobility of the UAV,the learning factor of Q-learning is adaptively adjusted according to the change of network topology,and the trade-off optimization of the convergence speed of the algorithm and the exploration ability of the optimal solution is realized.Simulation experiments show that the proposed algorithm can realize the trade-off optimization of network communication conflict and convergence speed,and can solve the problem of resource allocation decision and fast-changing topology adaptation in post-disaster high-dynamic scenarios.

    Drone identification based on the normalized cyclic prefix correlation spectrum
    ZHANG Hanshuo, LI Tao, LI Yongzhao, WEN Zhijin
    2024, 51(2):  68-75.  doi:10.19665/j.issn1001-2400.20230704
    Abstract ( 38 )   HTML( 24 )   PDF (1621KB) ( 24 )   Save

    Radio-frequency(RF)-based drone identification technology has the advantages of long detection distance and low environmental dependence,so that it has become an indispensable approach to monitoring drones.How to identify a drone effectively at the low signal-to-noise ratio(SNR) regime is a hot topic in current research.To ensure excellent video transmission quality,drones commonly adopt orthogonal frequency division multiplexing(OFDM) modulation with cyclic prefix(CP) as the modulation of video transmission links.Based on this property,we propose a drone identification algorithm based on the convolutional neural network(CNN) and normalized CP correlation spectrum.Specifically,we first analyze the OFDM symbol durations and CP durations of drone signals,on the basis of which the normalized CP correlation spectrum is calculated.When the modulation parameters of a drone signal match the calculated normalized CP correlation spectrum,several correlation peaks will appear in the normalized CP correlation spectrum.The positions of these peaks reflect the protocol characteristics of drone signals,such as frame structure and burst rules.Finally,for identifying drones,a CNN is trained to extract these characteristics from the normalized CP correlation spectrum.In this work,a universal software radio peripheral(USRP) X310 is utilized to collect the RF signals of five drones to construct the experimental dataset.Experimental results show that the proposed algorithm performs better than spectrum-based and spectrogram-based algorithms,and it remains effective at low SNRs.

    Study of the parallel MoM on a domestic heterogeneous DCU platform
    JIA Ruipeng, LIN Zhongchao, ZUO Sheng, ZHANG Yu, Y...
    2024, 51(2):  76-83.  doi:10.19665/j.issn1001-2400.20230504
    Abstract ( 26 )   HTML( 20 )   PDF (2873KB) ( 20 )   Save

    In view of the current development trend of the domestic supercomputer CPU+DCU heterogeneous architecture,the research on the CPU+DCU massively heterogeneous parallel higher-order method of moments is carried out.First,the basic implementation strategy of DCU to accelerate the calculation of the method of moments is given.Based on the load balancing parallel strategy of the isomorphic parallel moment of methods,an efficient heterogeneous parallel programming framework of "MPI+openMP+DCU" is proposed to address the problem of mismatch between computing tasks and computing power.In addition,the fine-grained task division strategy and asynchronous communication technology are adopted to optimize the design of the pipeline for the DCU computation process,thus realizing the overlapping of computation and communication and improving the acceleration performance of the program.The accuracy of the CPU+DCU heterogeneous parallel moment of methods is verified by comparing the simulation results with those by the finite element method.The scalability analytical results based on the domestic DCU heterogeneous platform show that the implemented CPU+DCU heterogeneous co-computing program can obtain 5.5~7.0 times acceleration effect at different parallel scales,and that the parallel efficiency reaches 73.5% when scaled from 360 nodes to 3600 nodes(1,036,800 cores in total).

    Obstacle avoidance algorithm for the mobile robot with vibration suppression
    WU Tingming, WU Xianyun, DENG Liang, LI Yunsong
    2024, 51(2):  84-95.  doi:10.19665/j.issn1001-2400.20230701
    Abstract ( 29 )   HTML( 22 )   PDF (5324KB) ( 22 )   Save

    Aiming at the problem that dynamic obstacle avoidance algorithms for indoor mobile robots are prone to local dead zones,an improved vector field histogram(VFH) dynamic obstacle avoidance algorithm is proposed.First,according to traditional VFH-class algorithms,a path lenght cost and an evaluation index of trough width are introduced to the candidate evaluation function of the trough to reduce the probability of the mobile robot falling into local dead zones and to improve path smoothness.Second,in view of the problem that local obstacle avoidance algorithms are limited to local environment and oscillate back and forth near obstacles easily,an oscillation evaluation function is introduced with an oscillation evaluation curve drawn by calculating the weighted Euclidean distances from the pose of the mobile robot to the starting and ending points.Automatic peak detection and a first-order forward difference curve are employed to obtain the oscillation positions,and then the oscillation suppression is taken to make the mobile robot escape the local dead zones.Simulation results show that within 100 groups of simulation scenarios,the number of scenarios wherein the improved VFH algorithm falls into the local dead zones is reduced by 70 groups,the average number of planning iterations is decreased by 32.3 times,the average path length is reduced by 26.2%,and the average cumulative turning angle is declined by 79.6%.The algorithm can effectively reduce the cost of the local obstacle avoidance,improve the path smoothness and reduce the probability of falling into the dead zones in local special environment.

    Research on lightweight and feature enhancement of SAR image ship targets detection
    GONG Junyang, FU Weihong, FANG Houzhang
    2024, 51(2):  96-106.  doi:10.19665/j.issn1001-2400.20230407
    Abstract ( 44 )   HTML( 37 )   PDF (2728KB) ( 37 )   Save

    The accuracy of ship targets detection in sythetic aperture radar images is susceptible to the nearshore clutter.The existing detection algorithms are highly complex and difficult to deploy on embedded devices.Due to these problems a lightweight and high-precision SAR image ship target detection algorithm CA-Shuffle-YOLO(Coordinate Shuffle You Only Look Once) is proposed in this article.Based on the YOLO v5 target detection algorithm,the backbone network is improved in two aspects:lightweight and feature refinement.The lightweight module is introduced to reduce the computational complexity of the network and improve the reasoning speed,and a collaborative attention mechanism module is introduced to enhance the algorithm's ability to extract the detailed information on near-shore ship targets.In the feature fusion network,weighted feature fusion and cross-module fusion are used to enhance the ability of the model to fuse the detailed information on SAR ship targets.At the same time,the depth separable convolution is used to reduce the computational complexity and improve the real-time performance.Through the test and comparison experiments on the SSDD ship target detection dataset,the results show that the detection accuracy of CA-Shuffle-YOLO is 97.4%,the detection frame rate is 206FPS,and the required computational complexity is 6.1GFlops.Compare to the original YOLO v5,the FPS of our algorithm is 60FPS higher with the required computational complexity of our algorithm being only the 12% that of the ordinary YOLOv5.

    Beacon-aided CPD indoor positioning method for the BeiDou pseudo-satellite
    ZHANG Heng, YU Baoguo, PAN Shuguo
    2024, 51(2):  107-115.  doi:10.19665/j.issn1001-2400.20230409
    Abstract ( 24 )   HTML( 20 )   PDF (3360KB) ( 20 )   Save

    In order to solve the problem of high-precision positioning of the BeiDou pseudo-satellite signal in indoor small-scale space,how to save the cost of network construction on the basis of small-scale space to improve the timeliness and positioning accuracy of indoor positioning technology is an important link in the future.In this paper,a method of BeiDou pseudo-satellite carrier phase difference(CPD) localization assisted by indoor node beacons is proposed,which fully combines the characteristics of small-scale space in indoor environment.First,the problem of large-scale fingerprint construction is transformed into a fingerprint beacon,and the concept of indoor node beacon is proposed.The connection between small-scale space and surrounding space is realized by beacon nodes,and the construction and processing of the beacon characteristic spectrum based on the carrier-to-noise ratio(CN0) and carrier phase are analyzed.Then,based on the indoor node beacon,the process of position estimation based on CPD is presented.Finally,a location search algorithm considering the constraints of pedestrian location and velocity space is proposed based on particle swarm optimization(PSO).Experimental results in real environment show that the dynamic positioning accuracy of 30cm and the positioning accuracy of 25cm in a suspended state can be achieved by the indoor node beacon.Compared with inertial navigation,it has a more relaxed attitude condition and is suitable for high-precision positioning processing in small-scale space.The proposed algorithm has a better applicability in small-scale space.

    Research on the construction and application of polar codes for shallow water acoustic communication
    XING Lijuan, LI Zhuo, HUANG Yanbiao
    2024, 51(2):  116-125.  doi:10.19665/j.issn1001-2400.20230505
    Abstract ( 27 )   HTML( 20 )   PDF (2313KB) ( 20 )   Save

    To realize high speed and high-reliability communication in shallow water environments,the performance of polar code encoding and decoding technology in shallow water acoustic communication is studied.The Monte Carlo algorithm construction is used to complete the construction of polar codes on the time-invariant,quasi-stationary,and time-variant channel models established based on the ray acoustic theory,and the complexity and performance are compared with those of the channel polarization and channel degradation construction algorithms and the base-symmetric extended polarization weight construction algorithm.The constructed polar code is adopted as the channel coding scheme for the underwater acoustic communication system based on Orthogonal Frequency Division Multiplexing and the decoding scheme uses a Cyclic Redundancy Check-Aided Successive Cancellation List decoding algorithm.The performance of polar codes on these three channels is determined by simulation in comparison with the performance of Low-Density Parity Check codes with the same code length and code rate.Experimental results show that in these three channels and the range of the signal-to-noise ratio of interest,polar codes have a gain of about 0.5 dB ~ 1.2 dB relative to Low-Density Parity Check codes.Simulation comparison results of the three channels show that polar codes based on channel construction coding have better gain effects in harsh channel environments compared to Low-Density Parity Check codes,and that polar codes have a lower encoding and decoding complexity,which proves the competitiveness and broad application prospect of the polar code in energy and resource-limited shallow sea acoustic communication.

    Efficient seed generation method for software fuzzing
    LIU Zhenyan, ZHANG Hua, LIU Yong, YANG Libo, WANG ...
    2024, 51(2):  126-136.  doi:10.19665/j.issn1001-2400.20230901
    Abstract ( 30 )   HTML( 22 )   PDF (1912KB) ( 22 )   Save

    As one of the effective ways to exploit software vulnerabilities in the current software engineering field,fuzzing plays a significant role in discovering potential software vulnerabilities.The traditional seed selection strategy in fuzzing cannot effectively generate high-quality seeds,which results in the testcases generated by mutation being unable to reach deeper paths and trigger more security vulnerabilities.To address these challenges,a seed generation method for efficient fuzzing based on the improved generative adversarial network(GAN) is proposed which can flexibly expand the type of seed generation through encoding and decoding technology and significantly improve the fuzzing performance of most applications with different input types.In experiments,the seed generation strategy adopted in this paper significantly improved the coverage and unique crashes,and effectively increased the seed generation speed.Six open-sourced programs with different highly-structured inputs were selected to demonstrate the effectiveness of our strategy.As a result,the average branch coverage increased by 2.79%,the number of paths increased by 10.35% and additional 86.92% of unique crashes were found compared to the original strategy.

    Integration of pattern search into the grasshopper optimization algorithm and its applications
    XIAO Yixin, LIU Sanyang
    2024, 51(2):  137-156.  doi:10.19665/j.issn1001-2400.20230602
    Abstract ( 29 )   HTML( 27 )   PDF (2873KB) ( 27 )   Save

    In the process of applying intelligent optimization algorithms to solve complex optimization problems,balancing exploration and exploitation is of great significance in order to obtain optimal solutions.Therefore,this paper proposes a grasshopper optimization algorithm that integrates pattern search to address the limitations of traditional grasshopper optimization algorithm,such as low convergence accuracy,weak search capability,and susceptibility to local optima in handling complex optimization problems.First,a Sine chaotic mapping is introduced to initialize the positions of individual grasshopper population,reducing the probability of individual overlap and enhancing the diversity of the population in the early iterations.Second,the pattern search method is employed to perform local search for the currently found optimal targets in the population,thereby improving the convergence speed and optimization accuracy of the algorithm.Additionally,to avoid falling into local optima in the later stages of the algorithm,a reverse learning strategy based on the imaging of convex lenses is introduced.In the experimental section,a series of ablative experiments is conducted on the improved grasshopper algorithm to validate the independent effectiveness of each strategy,including the Sine chaotic mapping,pattern search,and reverse learning.Simulation experiments are performed on two sets of test functions,with the results analyzed using the Wilcoxon rank-sum test and Friedman test.Experimental results consistently demonstrate that the fusion mode search strategy improved grasshopper algorithm exhibits significant enhancements in both convergence speed and optimization accuracy.Furthermore,the application of the improved algorithm to mobile robot path planning further validates its effectiveness.

    Hyperspectral image denoising based on tensor decomposition and adaptive weight graph total variation
    CAI Mingjiao, JIANG Junzheng, CAI Wanyuan, ZHOU Fa...
    2024, 51(2):  157-169.  doi:10.19665/j.issn1001-2400.20230412
    Abstract ( 27 )   HTML( 21 )   PDF (2394KB) ( 21 )   Save

    During the acquisition process of hyperspectral images,various noises are inevitably introduced due to the influence of objective factors such as observation conditions,material properties of the imager,and transmission conditions,which severely reduces the quality of hyperspectral images and limits the accuracy of subsequent processing.Therefore,denoising of hyperspectral images is an extremely important preprocessing step.For the hyperspectral image denoising problem,a denoising algorithm,which is based on low-rank tensor decomposition and adaptive weight graph total variation regularization named LRTDGTV,is proposed in this paper.Specifically,Low-rank tensor decomposition is used to characterize the global correlation among all bands,and adaptive weight graph total variation regularization is adopted to characterize piecewise smoothness property of hyperspectral images in the spatial domain and preserve the edge information of hyperspectral images.In addition,sparse noise,including stripe noise,impulse noise and deadline noise,and Gaussian noise are characterized by l1-norm and Frobenius-norm,respectively.Thus,the denoising problem can be formulated into a constrained optimization problem involving low-rank tensor decomposition and adaptive weight graph total variation regularization,which can be solved by employing the augmented Lagrange multiplier(ALM) method.Experimental results show that the proposed hyperspectral image denoising algorithm can fully characterize the inherent structural characteristics of hyperspectral images data and has a better denoising performance than the existing algorithms.

    Adaptivedensity peak clustering algorithm
    ZHANG Qiang, ZHOU Shuisheng, ZHANG Ying
    2024, 51(2):  170-181.  doi:10.19665/j.issn1001-2400.20230604
    Abstract ( 35 )   HTML( 24 )   PDF (3821KB) ( 24 )   Save

    Density Peak Clustering(DPC) is widely used in many fields because of its simplicity and high efficiency.However,it has two disadvantages:① It is difficult to identify the real clustering center in the decision graph provided by DPC for data sets with an uneven cluster density and imbalance;② There exists a "chain effect" where a misallocation of the points with the highest density in a region will result in all points within the region pointing to the same false cluster.In view of these two deficiencies,a new concept of Natural Neighbor(NaN) is introduced,and a density peak clustering algorithm based on the natural neighbor(DPC-NaN) is proposed which uses the new natural neighborhood density to identify the noise points,selects the initial preclustering center point,and allocates the non-noise points according to the density peak method to get the preclustering.By determining the boundary points and merging radius of the preclustering,the results of the preclustering can be adaptively merged into the final clustering.The proposed algorithm eliminates the need for manual parameter presetting and alleviates the problem of "chain effect".Experimental results show that compared with the correlation clustering algorithm,the proposed algorithm can obtain better clustering results on typical data sets and perform well in image segmentation.

    Study of EEG classification of depression by multi-scale convolution combined with the Transformer
    ZHAI Fengwen, SUN Fanglin, JIN Jing
    2024, 51(2):  182-195.  doi:10.19665/j.issn1001-2400.20230211
    Abstract ( 33 )   HTML( 26 )   PDF (2907KB) ( 26 )   Save

    In the process of using the deep learning model to classify the EEG signals of depression,aiming at the problem of insufficient feature extraction in single-scale convolution and the limitation of the convolutional neural network in perceiving the global dependence of EEG signals,a multi-scale dynamic convolution network module and the gated transformer encoder module are designed respectively,which are combined with the temporal convolution network,and a hybrid network model MGTTCNet is proposed to classify the EEG signals of patients with depression and healthy controls.First,multi-scale dynamic convolution is used to capture the multi-scale time-frequency information of EEG signals from spatial and frequency domains.Second,the gated transformer encoder is used to learn global dependencies in EEG signals,which effectively enhances the ability of the network to express relevant EEG signal features using the multi-head attention mechanism.Third,the temporal convolution network is used to extract temporal features available for EEG signals.Finally,the extracted abstract features are fed into the classification module for classification.The proposed model is experimentally validated on the public data set MODMA using the Hold-out method and the 10-Fold Cross Validation method,with the classification accuracy being 98.51% and 98.53%,respectively.Compared with the baseline single-scale model EEGNet,the classification accuracy of the proposed model is increased by 1.89% and 1.93%,the F1 value is increased by 2.05% and 2.08%,and the kappa coefficient values are increased by 0.0381 and 0.0385,respectively.Meanwhile,the ablation experiments verify the effectiveness of each module designed in this paper.

    Secure K-prototype clustering against the collusion of rational adversaries
    TIAN Youliang, ZHAO Min, BI Renwan, XIONG Jinbo
    2024, 51(2):  196-210.  doi:10.19665/j.issn1001-2400.20230305
    Abstract ( 26 )   HTML( 17 )   PDF (1874KB) ( 17 )   Save

    Aiming at the problem of data privacy leakage in cloud environment and collusion between cloud servers in the process of clustering,an cooperative secure K-prototype clustering scheme(CSKC) against the adversaries of rational collusion is proposed.First,considering that homomorphic encryption does not directly support nonlinear computing,secure computing protocols are designed based on homomorphic encryption and additive secret sharing to ensure that the input data and intermediate results are in the form of additive secret share,and to achieve accurate calculation of the security comparison function.Second,according to the game equilibrium theory,a variety of efficient incentive mechanisms are designed,and the mutual condition contract and report contract are constructed to constrain cloud servers to implement secure computing protocols honestly and non-collusively.Finally,the proposed protocols and contracts are analyzed theoretically,and the performance of the CSKC scheme is verified by experiment.Experimental results show that compared with the model accuracy in plaintext environment,the model accuracy loss of the CSKC scheme is controlled within 0.22%.

    New method for calculating the differential-linear bias of the ARX cipher
    ZHANG Feng, LIU Zhengbin, ZHANG Jing, ZHANG Wenzhe...
    2024, 51(2):  211-223.  doi:10.19665/j.issn1001-2400.20230404
    Abstract ( 27 )   HTML( 19 )   PDF (1106KB) ( 19 )   Save

    The ARX cipher consists of three basic operations,additions,rotations and XORs.Statistical analysis is currently used to calculate the bias of the ARX cipher differential-linear distinguishers.At CRYPTO 2022,NIU et al.gave a method for evaluating the correlation of the ARX cipher differential-linear distinguishers without using statistical analysis.They gave a 10-round differential-linear distinguisher for SPECK32/64.This paper gives the definition of differential-linear characteristics.It presents the first method for calculating the bias of differential-linear distinguishers using differential-linear characteristics based on the methods by BLONDEAU et al.and BAR-ON et al.Also,a method for searching for differential-linear characteristics based on Boolean Satisfiability Problem(SAT) automation techniques is proposed,which is a new method for calculating the bias of the ARX cipher differential-linear distinguisher without statistical analysis.As an application,the bias of the 10-round differential-linear distinguisher for SPECK32/64 given by NIU et al.is calculated with the theoretical value 2-15.00 obtained,which is very close to the experimental value 2-14.90 from the statistical analysis and better than the theoretical value 2-16.23 given by NIU et al.Also,the first theoretical value 2-8.41 for the bias of the 9-round differential-linear distinguisher for SIMON32/64 is given,which is close to the experimental value 2-7.12 obtained by statistical analysis.Experimental results fully demonstrate the effectiveness of this method.

    Improved data sharing scheme based on conditional broadcast proxy re-encryptionn
    ZHAI Sheping, LU Xianjing, HUO Yuanyuan, YANG Rui
    2024, 51(2):  224-238.  doi:10.19665/j.issn1001-2400.20230410
    Abstract ( 36 )   HTML( 20 )   PDF (2012KB) ( 20 )   Save

    Traditional conditional broadcast proxy re-encryption data sharing approaches over-rely on untrustworthy third-party proxy servers,which leads to issues of a low efficiency,data security and privacy leaks.To address the above problems,this paper proposes an information security protection scheme that combines conditional broadcast proxy re-encryption with blockchain consensus mechanisms.First,to solve the single point of failure and collusion attacks of individual proxy servers,this scheme uses blockchain nodes to take turns to act as proxy servers.At the same time,it selects high-credibility proxy servers to participate in re-encryption through the Delegated Proof of Stake(DPoS) consensus algorithm that integrates credibility mechanisms,greatly reducing the risks of the single point of failure and collusion attacks.Second,to address the high permission issue of proxy servers using re-encryption keys,this paper introduces the threshold cryptosystem concept and splits the re-encryption key into multiple fragments distributed across different proxy servers.In this way,any single proxy server is unable to decrypt data independently,thus effectively improving the security of the re-encryption process.Finally,through the analysis of the security,correctness and credibility of the scheme,it is demonstrated that this scheme can effectively solve security vulnerabilities in traditional schemes.Related simulation experimental results also prove that compared with existing data sharing schemes,this scheme has significant advantages in ensuring data security while having lower computational costs.

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