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Verifiable and Forward-Secure Multi-Keyword Query in Internet of Medical Things

IEEE Internet of Things Journal 2025年第13期12卷 23809-23822页

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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Physics-Informed Neural Networks for Parametric Compressible Euler Equations

arXiv

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arXiv 2023年

作者： Wassing, Simon Langer, Stefan Bekemeyer, Philipp German Aerospace Center Institute for Aerodynamics and Flow Technology Center for Computer Applications in Aerospace Science and Engineering Lilienthalplatz 7 Braunschweig38108 Germany

The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been successfully employed for solving partial differential equations by incorporating the equations into a loss function that is minimized during the training of a neural network. This approach yields a so-called physicsinformed neural network. It is not based upon classical discretizations, such as finite-volume or finite-element schemes, and can even address parametric problems in a straightforward manner. This has raised the question, whether physics-informed neural networks may be a viable alternative to conventional methods for computational fluid dynamics. In this article we introduce an adaptive artificial viscosity reduction procedure for physics-informed neural networks enabling approximate parametric solutions for forward problems governed by the stationary two-dimensional Euler equations in sub- and supersonic conditions. To the best of our knowledge, this is the first time that the concept of artificial viscosity in physics-informed neural networks is successfully applied to a complex system of conservation laws in more than one dimension. Moreover, we highlight the unique ability of this method to solve forward problems in a continuous parameter space. The presented methodology takes the next step of bringing physics-informed neural networks closer towards realistic compressible flow applications. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Artificial Viscosity in Physics-Informed Neural Networks for Parametric Compressible Flows

SSRN

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SSRN 2023年

作者： Wassing, Simon Langer, Stefan Bekemeyer, Philipp German Aerospace Center Institute for Aerodynamics Flow Technology Center for Computer Applications in Aerospace Science and Engineering Lilienthalplatz 7 Braunschweig38108 Germany

The numerical approximation of solutions to the compressible Euler and Navier-stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been successfully employed for solving partial differential equations by incorporating the equations into a loss function that is minimized during the training of a neural network. This approach yields a so-called physics-informed neural network. It is not based upon classical discretizations, such as finite-volume or finite-element schemes, and can even address parametric problems in a straightforward manner. This has raised the question, whether physics-informed neural networks may be a viable alternative to conventional methods for computational fluid dynamics. In this article we propose a physics-informed neural network training procedures to approximate steady-state solutions of boundary-value problems for the compressible Euler equations. It turns out that the addition of artificial dissipation during the training process is important to avoid unphysical results. A method for reducing this additional numerical viscosity during the training is presented. Furthermore, we showcase how this approach can be combined with parametric boundary conditions. Our results highlight the appearance of unphysical results when solving compressible flows with physics-informed neural networks and offer a new approach to overcome this problem. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Multi-party privacy-preserving decision tree training with a privileged party

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science China(Information sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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Heterogeneity Modeling with Flexibility-based Graph Neural Networks 22

Heterogeneity Modeling with Flexibility-based Graph Neural N...

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22nd IEEE International Symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Qin, Jisheng Tao, Tao Liu, Zijie Chen, Haibo Zhao, Shenghui Chuzhou University School of Computer and Information Engineering Anhui Chuzhou China Anhui University of Technology School of Computer Science and Technology Anhui Ma'anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology China

ISBN: (纸本)9798331509712

Graph Neural Networks (GNNs) have demonstrated robust performance in various graph-based machine learning tasks, especially in homophilous graphs, where edges tend to connect nodes with the same label. However, achieving consistent performance on heterophilous graphs remains challenging. Several works have extended GNN architectures to address the distinct characteristics of heterogeneous graphs. However, these methods often need to enhance the flexibility of handling feature combinations for the central node and its neighbors. In this paper, we introduce a novel approach to modeling heterogeneity with Flexibility-based Graph Convolutional Networks (FGCN). FGCN consists of two primary components: first, it independently encodes the central node and its neighbors, fusing them to obtain an output representation. Second, it aggregates nodes with the same label from a feature and neighbor perspective, imposing constraints on the transformation during graph structure propagation. This approach enhances the flexibility of combining the central node and neighbor representations. We conducted extensive experiments on eight real-world datasets, demonstrating that FGCN outperforms state-of-the-art (SOTA) baseline models. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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AceRoute: Adaptive Compute-Efficient FPGA Routing with Pluggable Intra-Connection Bidirectional Exploration 24

AceRoute: Adaptive Compute-Efficient FPGA Routing with Plugg...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Wei, Xinming Zhang, Ziyun Zou, Sunan Sun, Kaiwen Zhang, Jiahao Zhang, Jiaxi Fan, Ping Luo, Guojie School of Computer Science Peking University Beijing China DeePoly Technology Inc. Beijing China Undergraduate School of Electronics Engineering and Computer Science Peking University Beijing China Center for Energy-efficient Computing and Applications Peking University Beijing China

ISBN: (纸本)9798400710773

This paper introduces AceRoute, an adaptive compute-efficient FPGA router that tackles the long-standing issue of lengthy FPGA compilation times given complicated FPGA architectures and designs to synthesize. We thoroughly profile modern FPGA routing patterns and identify the runtime hotspot: routing bottleneck connections in congested designs. However, previous works on routing acceleration hardly target mitigating connection-wise routing difficulties by characterizing device resource expansions and shifting path-exploration modes of connections. In this work, we propose a bidirectional intra-connection routing paradigm for the first time, which efficiently navigates congested device regions by initiating searches from both source and sink nodes. This approach significantly outperforms traditional unidirectional exploration in congested conditions. Furthermore, for each connection to route, we develop an adaptive strategy to select the optimal search mode online between uni- and bi-directional based on the current congestion situation. Our approach is pluggable and versatile, allowing seamless integration into existing FPGA routers and providing instant speed improvements with merely several hundred lines of code. Evaluation on FPGA24 contest benchmarks shows that our router, powered by adaptive bidirectional search, achieves over 2.4× and 3.2× faster routing on average with a serial and a 2-thread parallel version, respectively, compared with established routing tools RWRoute and Vivado. Additionally, when integrated into typical partition-based inter-connection parallel routers, our approach overcomes their inherent load-balance problems and amplifies the speedup to RWRoute from 2.2× to up to 5×. © 2024 Copyright is held by the owner/author(s).

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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2024 International Conference on Decision Aid sciences and applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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