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PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing

IEEE Transactions on Cloud Computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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SEARCHING LOTTERY TICKETS IN GRAPH NEURAL NETWORKS: A DUAL PERSPECTIVE 11

SEARCHING LOTTERY TICKETS IN GRAPH NEURAL NETWORKS: A DUAL P...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Kun Liang, Yuxuan Wang, Pengkun Wang, Xu Gu, Pengfei Fang, Junfeng Wang, Yang Hefei China School of Software Engineering USTC China School of Data Science USTC China National University of Singapore Singapore

Graph Neural Networks (GNNs) have shown great promise in various graph learning tasks. However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly, posing obstacles to GNNs from scaling up to real-world applications. To tackle this issue, Graph Lottery Ticket (GLT) hypothesis articulates that there always exists a sparse subnetwork/subgraph with admirable performance in GNNs with random initialization. Such a pair of core subgraph and sparse subnetwork (called graph lottery tickets) can be uncovered by iteratively applying a novel sparsification method. While GLT provides new insights for GNN compression, it requires a full pretraining process to obtain graph lottery tickets, which is not universal and friendly to real-world applications. Moreover, the graph sparsification in GLT utilizes sampling techniques, which may result in massive information loss and aggregation failure. In this paper, we explore the searching of graph lottery tickets from a complementary perspective - transforming a random ticket into a graph lottery ticket, which allows us to more comprehensively explore the relationships between the original network/graph and their sparse counterpart. Compared to GLT, our proposal helps achieve a triple-win situation of graph lottery tickets with high sparsity, admirable performance, and good explainability. More importantly, we rigorously prove that our model can eliminate noise and maintain reliable information in substructures using the graph information bottleneck theory. Extensive experimental results on various graph-related tasks validate the effectiveness of our framework. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

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Computer Modeling in engineering & sciences 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： Key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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Research on Low-Cost Deployment and Low Energy Consumption Auxiliary Communication Methods Based on Unmanned Aerial Vehicles 2

Research on Low-Cost Deployment and Low Energy Consumption A...

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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Zhao, Yan Yang, Xiongfei Digital Guangdong Network Construction Co. Ltd Guangzhou China School of Data Science and Software Engineering Wuzhou University Wuzhou China

ISBN: (纸本)9798350307597

Considering the limited communication of ground IoT devices in wide-area agricultural reclamation areas, this paper proposes a method of unmanned aerial vehicle (UAV) collaborative coverage to minimize the number of UAVs and UAV's energy consumption while meeting the needs of regional signal coverage. Firstly, with the goal of maximizing coverage area, the optimization of UAV cooperative coverage strategy is achieved under constraints such as UAV multi machine interference, UAV energy constraints, and node received signal-to-noise ratio. Through simulation experiments, the impact of UAV flight speed on communication energy consumption in different distance scenarios is analyzed, and energy consumption optimization is achieved in the scenario of maximizing UAV coverage area. Therefore, the multi machine collaborative coverage methods proposed in this article can meet the needs of UAV assisted enhanced communication coverage scenarios, and to some extent, improve the coverage quality of weak signal areas. © 2024 IEEE.

关键词： Aircraft communication

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Time Series data Cleaning under Expressive Constraints on Both Rows and Columns 40

Time Series Data Cleaning under Expressive Constraints on Bo...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module 4

Improving Event Coreference Resolution Using Prompt Learning...

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4th International Conference on Computer science and Blockchain, CCSB 2024

作者： Zhang, Mengyao Long, Jun Huang, Wenti School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (纸本)9798350356502

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments. © 2024 IEEE.

关键词： Semantics

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FedTop:a constraint-loosed federated learning aggregation method against poisoning attack

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Frontiers of Computer science 2024年第5期18卷 233-235页

作者： Che WANG Zhenhao WU Jianbo GAO Jiashuo ZHANG Junjie XIA Feng GAO Zhi GUAN Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China China Unicom Beijing 100033China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].

关键词： constraint loose aggregation

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