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A Blind Batch Encryption and public Ledger-Based Protocol for Sharing Sensitive data

China Communications 2024年第1期21卷 310-322页

作者： Zhiwei Wang Nianhua Yang Qingqing Chen Wei Shen Zhiying Zhang School of Computer Sciences Nanjing University of Posts and TelecommunicationsNanjing 210023China Dept.of Intelligent Science and Information Law East China University of Political Science and LawShanghai 201620China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal UniversityHaikou 571158China State Key Laboratory of Public Big Data Guizhou UniversityGuizhou 550025China Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Research Institute of Beijing University of Aeronautics and AstronauticsKunming 650233

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be *** owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the *** prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file *** also provide security analysis in the context of four typical attacks.

关键词： blind batch encryption data sharing onetime adaptive access public ledger security and privacy

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Resisting Membership Inference Attacks by Dynamically Adjusting Loss Targets

Resisting Membership Inference Attacks by Dynamically Adjust...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Ma, Xihua Tian, Youliang Ding, Zehua Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Institute of Cryptography and Data Security Guizhou University Guiyang550025 China

ISBN: (纸本)9798350327380

Machine learning (ML) models are susceptible to membership inference attacks (MIAs), which aim to infer whether a particular sample was involved in model training. Previous research suggests that the difference in loss distribution between member and non-member sample is an essential factor of MIAs. In the latest mitigation strategies to reduce the loss distribution discrepancy, the model owner must manually set a loss target for the training task. However, this can be challenging due to differences in datasets and model structures. We propose a new mitigation strategy based on existing studies, which can dynamically adjust the loss target during the training process according to the model structure and dataset characteristics, to achieve a reduced loss gap. We extensively evaluated our strategy in white-box and black-box environments, respectively. Our experimental results show that our approach avoids the problem of setting loss targets and even improves the model's resistance to attacks in most cases. Specifically, the accuracy of the attacks is reduced by an average of 4.92% and 11.3% in the black-box and white-box environments, respectively. © 2023 IEEE.

关键词： Machine learning

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Prescribed Performance-Based Finite-Time Neural Adaptive Backstepping Control for the Chaotic PMSM

Prescribed Performance-Based Finite-Time Neural Adaptive Bac...

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2022 International Conference on Mechanical and Electronics Engineering, ICMEE 2022

作者： Wu, Fengbin Zhang, Junxing Li, Shaobo Li, Shaoyang Wu, Xiao Wang, Shuai College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9781665492201

This paper presents a prescribed performance-based finite-time neural adaptive backstepping control scheme for the chaotic permanent magnet synchronous motor (PMSM). Specifically, an error transformation coupled with a prescribed performance function is introduced to guarantee that the tracking error keeps within a defined bound. The finite-time stability theory and backstepping framework are further combined to design finite-time adaptive laws and controllers. Then, it is shown that all signals are ultimately bounded in finite time and the tracking error can converge to a defined region in finite time. Finally, simulation results are presented to verify the feasibility of the proposed controller. © 2022 IEEE.

关键词： Backstepping

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Core Loss Prediction Using a PC-GBT Model Based on Permutation Feature Importance and Grid Search Optimization

Core Loss Prediction Using a PC-GBT Model Based on Permutati...

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Algorithms, Computing and Machine Learning (CACML), Asia Conference on

作者： Yunhua Ran Shaobo Li College of Computer Science and Technology Guizhou University Guiyang State Key Laboratory of Public Big Data Guizhou University Guiyang

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

This study aims to predict core loss in magnetic materials using a PC-GBT model based on Permutation Feature Importance and Grid Search Optimization. The research analyzes the impact of three key factors—temperature, excitation waveform, and core material—on core loss, including both their individual and paired interactive effects, and evaluates the significance of each factor. Descriptive statistical analysis, distribution characteristics analysis, and univariate analysis were conducted to establish the relationships between core loss and these factors. The results indicate a positive correlation between core loss and both excitation waveform and material, while a negative correlation is observed with temperature. A two-factor analysis further explores the synergistic effects of temperature, material, and excitation waveform on core loss. Additionally, flux density data was extracted, and potential features were identified with reference to the core loss separation model and two modified Steinmetz equations. Benchmark model comparisons reveal that the Gradient Boosted Trees (GBT) model outperforms Support Vector Machines (SVM) in terms of prediction accuracy and R 2 values. The development of the PC-GBT model includes Permutation Feature Importance analysis and Grid Search Optimization to identify the optimal hyperparameter configuration. Ultimately, the PC-GBT model demonstrates a lower mean error and an improved R2 value compared to the GBT model, confirming the effectiveness of the PC-GBT model.

关键词： Support vector machines Adaptation models Temperature Correlation Core loss Predictive models Feature extraction Mathematical models Magnetic materials Optimization

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Variational Neural Inference Enhanced Text Semantic Communication System

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China Communications 2024年第7期21卷 50-64页

作者： Zhang Xi Zhang Yiqian Li Congduan Ma Xiao School of Electronics and Communication Engineering Sun Yat-Sen UniversityShenzhen 518107China Guizhou University State Key Laboratory of Public Big Data Guiyang 550025China Shenzhen Key Laboratory of Navigation and Communication Integration Shenzhen 518107China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,*** efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and ***,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart ***,existing work tends to prioritize explicit semantics,neglecting the potential of implicit *** paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder *** propose a novel semantic communication system with variational neural inference for text ***,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received *** information is then utilized to assist in the decoding *** simulation results show a significant enhancement in system performance and improved robustness.

关键词： deep learning semantic communication variational neural inference

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RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pedestrian Detection

RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pe...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Juanmin Wang Kun Huang Jianyong Pi State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang Guizhou China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Dense pedestrian detection is a key research direction in the field of computer vision, which plays a significant role in large-scale crowded public spaces. It provides strong technical support for security assurance, traffic management, and behavioral analysis in public areas. To address the issue of missed detection of small-sized occluded pedestrians in dense scenarios, this paper proposes an improved detection algorithm based on YOLOv8, named RUP2S-YOLO. For the backbone network, the RFCBAM module is used to replace the convolution module to enhance the capability of capturing subtle features and better extract the characteristics of small-scale pedestrians; for the C2f module, the UKMS module is designed as a substitution aiming to improve the model's capacity to process information of different scales; for the detection head, the P2S module is introduced for the accurate positioning of small-scale targets. Experiments on dense pedestrian datasets show that the improved RUP2S-YOLO outperforms the original algorithm in terms of performance, achieving an AP50 of 82.8%, and an AP50:95 of 55.8%, which represents an increase of 2.2% and 2.8% respectively compared to the original algorithm. Meanwhile, in comparison with other leading pedestrian detection models, the improved YOLOv8 algorithm presented in this article also demonstrates competitive advantages.

关键词： YOLO Seminars Pedestrians Accuracy Feature extraction Security data mining

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Fuzzy Deduplication Scheme Supporting Pre-verification of Label Consistency 17th

Fuzzy Deduplication Scheme Supporting Pre-verification of L...

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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Tang, Zehui Zeng, Shengke Li, Tao Cheng, Shuai Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu610039 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Computer Science Qufu Normal University Rizhao276825 China

ISBN: (纸本)9783031455124

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of pre-verification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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Assisted diagnosis of breast cancer based on federated learning and improved DenseNet 5

Assisted diagnosis of breast cancer based on federated learn...

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5th International Conference on computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Xiang, Hexia Li, Jingjie Sun, Yongkai Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

Breast cancer is one of the common cancers worldwide, and how to identify breast cancer as early as possible plays an important role in its later diagnosis and treatment. However, relying only on data from a single organisation can limit the ability of trained neural networks to meet the performance demands of medical diagnostic services, and centralised learning that collects data from decentralised organisations can lead to privacy leakage. To address the above challenges, this paper proposes an assisted diagnosis model for breast cancer combining federated learning and improved DenseNet. Specifically, in contrast to mainstream centralised learning, this paper introduces federated learning to integrate multiparty data and effectively prevent privacy leakage. Each client sends the local model to the central server for aggregation, and then the global model obtained from the aggregation is sent to each client for training, thus achieving collaborative training of neural network models. In addition, in order to enhance the ability to capture feature information, this paper incorporates a Dynamic Convolution module into the densely connected network to improve the diagnostic accuracy without significantly increasing the size of the network model. The experimental results show that the proposed method has better diagnostic performance in breast cancer diagnosis compared with other methods. © 2024 SPIE.

关键词： Information leakage

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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