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Journal of network Intelligence 2025年第1期10卷 541-558页

作者： Xu, Peiming Jiang, Yixin Kuang, Xiaoyun Luo, Yu Chen, Jiahui Guangdong Provincial Key Laboratory of Power System Network Security CSG Guangzhou510000 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China

High utility itemset mining is a research topic of the pattern mining domain, which has been widely applied in reality during the past decade. Although most high utility itemset mining algorithms show significant execution performance regarding runtime and memory usage, numerous results are useless for users. Targeted high utility itemset mining can reduce the number of high utility itemsets and improve the efficiency of pattern analysis. However, the targeted high utility itemset mining task aims to find targeted high utility itemsets with as many items as possible. Users are not that interested in long results in actual scenarios. Therefore, in this article, we first formulate the problem and then address this issue by proposing a new algorithm named THL for mining targeted high utility itemset within length constraints, based on utility list structure. Furthermore, to improve the performance of the novel algorithm, THL adopts several efficiency pruning strategies, including length and utility aspects. Finally, we conduct an extensive experimental evaluation to compare the SOTA algorithms with THL on several datasets. © 2025, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： data mining high utility itemset length constraint targeted items

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Community-Preserving Social Graph Release with Node Differential Privacy

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Journal of computer Science & Technology 2023年第6期38卷 1369-1386页

作者：张森倪巍伟付楠 School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 211189China

The goal of privacy-preserving social graph release is to protect individual privacy while preserving data *** structure,which is an important global pattern of nodes,is a crucial data utility as it is fundamental to many graph analysis ***,most existing methods with differential privacy(DP)commonly fall into edge-DP to sacri-fice security in exchange for ***,they reconstruct graphs from the local feature-extraction of nodes,resulting in poor community *** by this,we develop PrivCom,a strict node-DP graph release algorithm to maximize the utility on the community structure while maintaining a higher level of *** this algorithm,to reduce the huge sensitivity,we devise a Katz index based private graph feature extraction method,which can capture global graph structure features while greatly reducing the global sensitivity via a sensitivity regulation ***,under the condition that the sensitivity is fixed,the feature captured by the Katz index,which is presented in matrix form,requires privacy budget *** a result,plenty of noise is injected,mitigating global structural *** bridge this gap,we de-sign a private eigenvector estimation method,which yields noisy eigenvectors from extracted low-dimensional ***,a dynamic privacy budget allocation method with provable utility guarantees is developed to preserve the inherent relationship between eigenvalues and eigenvectors,so that the utility of the generated noise Katz matrix is well ***,we reconstruct the synthetic graph via calculating its Laplacian with the noisy Katz *** results confirm our theoretical findings and the efficacy of PrivCom.

关键词： differential privacy social graph community structure private eigenvector

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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Measurement Manifolds Shape Clustering algorithms Synthetic data

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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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Science China(information Sciences) 2023年第6期66卷 258-266页

作者： Junjiang XIANG Qing PAN Zhengrong ZHANG Songnian FU Yuwen QIN Advanced Institute of Photonics Technology School of Information EngineeringGuangdong University of Technology Guangdong Provincial Key Laboratory of Photonics Information Technology Guangdong University of Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and InformationGuangxi University

To meet the challenge of camouflaged object detection （COD）,which has a high degree of intrinsic similarity between the object and background,this paper proposes a double-branch fusion network（DBFN）with a parallel attention selection mechanism （PASM）.In detail,a schismatic receptive field block（SRF）combined with an attention mechanism for low-level information is performed to learn texture features in one branch,and an integration of the SRF,a hybrid attention mechanism （HAM）,and a depth feature polymerization module （DFPM）is employed for high-level information to extract detection features in the other ***,both texture features and detection features are input into the PASM to acquire selective expression ***,the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling （ASPP）and a residual channel attention block （RCAB）being applied *** results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics,i.e.,mean absolute error （MAE）,weighted F βmeasure （Fβω）,structural measure （Sα）,and E-measure （Eφ）

关键词： camouflaged object detection attention mechanism feature extraction feature aggregation texture information fuzzy boundary

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural network Training Based on Functional Encryption

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computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Code-Based Conjunction Obfuscation

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Chinese Journal of Electronics 2023年第2期32卷 237-247页

作者： ZHANG Zheng ZHANG Zhuoran ZHANG Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangdong Province Key Laboratory of Information Security Technology

The pattern-matching problem with wildcards can be formulated as a conjunction where an accepting string is same as the pattern for all non-wildcards. A scheme of conjunction obfuscation is a algorithm that “encrypt” the pattern to prevent some adversary from forging any accepting string. Since 2013, there are abundant works about conjunction obfuscation which discussed with weak/strong functionality preservation and distributed black-box security. These works are based on generic group model, learning with error assumption,learning with noise assumption, etc. Our work proposes the first conjunction obfuscation with strong functionality preservation and distributed black-box security from a standard assumption. Our scheme with some parameter constraints can also resist some related attacks such as the information set decoding attack and the structured error arrack.

关键词： computers Quantum computing Computational modeling Closed box Resists Linear codes Mathematical models

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LDD-YOLO: An Improved Lightweight Detection Method for Steel Surface Defects Based on YOLOv8

LDD-YOLO: An Improved Lightweight Detection Method for Steel...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Yuechen Li, Aimin Li, Zhiyao Kong, Xiaotong Li, Wenqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Steel is an indispensable raw material in the industrial field, steel surface defects seriously affect the quality of steel, in recent years a lot of research has been carried out on the detection of steel surface defects. Existing steel defect detection methods are unable to fully mine the underlying feature information of the target image and do not achieve a dynamic balance between accuracy and speed. To address the above problems, this paper proposes an optimised target detection algorithm based on YOLOv8. First, we proposed the DMCA module, which combines the ideas of deformable convolution and multi-channel self-attention mechanism. We developed a strengthen self-attention module to enhance the process of deformable convolutional generation of offsets, so that the model can better adapt to the complex shapes of different defective targets and extract features at a deeper level. Secondly, using the idea of LKA (Large Kernel Attention), we propose the LF-MSPP lightweight module with long-range dependence and adaptive capability to capture the tele-relationships with small computational cost and parameters, improved the problem of missing defective feature information. Finally, we replaced the head of the original YOLOv8 with a Dynamic Head and used the split attention mechanism to improve the head detection capabilities while ensuring lightweight. We conduct extensive experiments on the widely used Northeastern University steel defect dataset NEU-DET. Experimental results show that the improved model improves mAP@50, mAP@50-95, AP and AR indicators by 2.4%, 2.0%, 6.1%, and 3.4% respectively compared with the original YOLOv8 model, and the number of model parameters is reduced by 11.2%. The improved model is also better than mainstream defect target detection models such as SSD, Retinanet, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8, etc, and can better meet the accuracy and speed requirements of actual industrial production for steel surface defect detection mode

关键词： Steel research

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Research and Application of Speech Transmission Index in Acoustic Leakage Detection 17

Research and Application of Speech Transmission Index in Aco...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Hao, Qiuyun Wang, FuQiang Zhang, Peng Ma, XiaoFeng Zhang, Jianqiang Zhao, Wei Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331507398

Acoustic information in confidential premise can be leaked through channels such as walls, pipes, doors and windows. Acoustic leakage level needs to be measured through specific indicators. Acoustic leakage evaluation indicator includes subjective evaluation indicator and objective evaluation indicator. The subjective evaluation indicator is a qualitative evaluation of acoustic leakage through subjective auditory perception of human ear, which can directly reflect the degree of acoustic leakage in confidential premise. At present, the objective evaluation indicator is mainly sound transmission loss (STL) to reflect objective degree of acoustic leakage in confidential premise. Therefore, it is relatively singular. Through study of Speech Transmission Index (STI), speech transmission index for public address systems (STIPA), which is a simplified form of FULL STI, is applied to acoustic leakage detection in this paper. STI has been proposed as an objective evaluation indicator and applied to acoustic leakage detection and acoustic information security. In order to confirm the effectiveness of objective evaluation indicator, a large number of related experimental studies have been conducted in the paper. Through experiments, it has been proven that STI can objectively evaluate level of acoustic leakage and level of acoustic information security in confidential premise. STI can better predict level of Chinese clarity under various complex transmission conditions. It provides a reliable basis for speech privacy of confidential premise and evaluation of architectural acoustic, sound reinforcement system and voice communication system. © 2024 IEEE.

关键词： information leakage

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Reputation based Adaptive Federated Learning in Medical Scenarios 9

Reputation based Adaptive Federated Learning in Medical Scen...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Lianhai Liu, Tianrui Wang, Yingxiaochun Wang, Xinlei Li, Qi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence. © 2024 IEEE.

关键词： Federated learning

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Infringement Detection and Traceability Scheme Based on Ciphertext Feature Extraction 9

Infringement Detection and Traceability Scheme Based on Ciph...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Wang, Lianhai Wang, Xinlei Liu, Tianrui Wang, Yingxiaochun Li, Qi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller's address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy. © 2024 IEEE.

关键词： Differential privacy

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