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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou 225127China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi'an 710071China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXining 810008China Faculty of Software Fujian Normal UniversityFuzhou 350108China

Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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Three-Channel Graph Convolutional Network 24

Three-Channel Graph Convolutional Network

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Meng, Lei Ye, Zhonglin Zhao, Haixing Yang, Yanlin Chen, Yang Wang, Zhaoyang College of Computer Qinghai Normal University Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9798350319934

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets. © 2022 IEEE.

关键词： Graph neural networks

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A practical adversarial attack on Graph Neural Networks by attacking single node structure 24

A practical adversarial attack on Graph Neural Networks by a...

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作者： Chen, Yang Ye, Zhonglin Zhao, Haixing Meng, Lei Wang, Zhaoyang Yang, Yanlin School of Computer Science Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai810008 China

ISBN: (纸本)9798350319934

In recent years, Graph Neural Networks (GNNs) have performed significantly in node classification tasks. Re-search on GNNs adversarial attacks has recently attracted attention. Many works have demonstrated that single node attacks can fool GNNs in extreme scenarios. Existing single node attacks can be divided into two main types: 1) attacking the node features;2) injecting a single fake node. However, they have several major drawbacks: 1) they set the attack node features, which makes them not efficient;2) they require higher privileges and don't have strict budget constraints, which makes them less concealable and easier to notice. In this paper, we propose an attack with lower privileges and strict budget constraints -Single Node Structure Attack (SNSA). Specifically, SNSA is set more efficient structure attacks and does not require higher privileges to change links. Compared to other attack models, the attack space of SNSA is small and not easily detected by GNNs. Furthermore, we study the influence of the attack node selection on SNSA and observe that it is optimal to use a gradient to select an attack node. Experiments on three common datasets demonstrate the effectiveness of our model. © 2022 IEEE.

关键词： Graph neural networks

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Robustness study of non-uniform scale-free hyper-network structure

Robustness study of non-uniform scale-free hyper-network str...

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2022 International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2022

作者： Zhou, Bin Ma, Xiujuan Ma, Fuxiang School of Computer Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

The relationship between the internal structure of the hyper-edge and robustness of the hypernetwork has not yet been investigated. Aiming at this problem, this paper proposes a hypernetwork capacity-load model with non-uniform load distribution. And obtained the robustness of the non-uniform scale-free hyper-network under different internal structures of the hyperedge. The simulation reveals that the robustness of the non-uniform scale-free hyper-network is closely related to the internal structure of the hyper-edge. The non-uniform scale-free hypernetwork is most robust when the nodes inside the hyper-edges are fully connected. The results show that the internal structure of hyper-edge has a large impact on the overall robustness of the non-uniform scale-free hyper-network. © 2022 Copyright for this paper by its authors.

关键词： Artificial intelligence

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Geological Disaster Named Entity Recognition with Small Samples Based on Data Augmentation and Prompt Engineering 24

Geological Disaster Named Entity Recognition with Small Samp...

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2024 7th International Conference on Computer information Science and Artificial Intelligence, CISAI 2024

作者： Ma, Xiangfei Li, Lin School of Computer Science Qinghai Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China Academy of Plateau Science and Sustainability Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400707254

This paper uses a large language model to perform generative data enhancement on the original small sample data by performing random synonym replacement and random mask filling operations. In accordance with the reasoning logic of the large language model, three prompt templates are designed and the reasons are explored. Experiments show that when the parameters remain unchanged, the data enhanced by this method has been greatly improved under the three prompt templates, alleviating the difficulty of low resources of geological disaster data. And by comparing the performance of different instructions under different learning rates, the fine-tuning learning rate range suitable for the field of geological disasters is summarized. The limitation is that it is constrained by local computing resources, which reduces the parameter scale of LLM, and the recognition performance is low for extremely long or complex texts. © 2024 Copyright held by the owner/author(s).

关键词： Engineering geology

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An Improved Capsule Network for Speech Emotion Recognition 1st

An Improved Capsule Network for Speech Emotion Recognition

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1st Southwest Data Science Conference, SDSC 2022

作者： Zhang, Huiyun Huang, Heming School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9783031233869

Speech Emotion Recognition (SER) is an important research content in the field of speech recognition, and it can make Human-Computer Interaction (HCI) more natural and harmonious. At present, the research of SER faces such problems as data scarcity and overfitting. To tackle these problems, an Improve Capsule Network (I-CapsNet) using Data Augmentation (DA) technique is proposed for efficient SER task. Firstly, the model I-CapsNet is used to reduce the number of parameters and retain more texture and background information. It can also retain the angle and discriminative information of the entity, thereby to relieve the overfiting. Secondly, noise is used to augment the baseline datasets (EMODB, CASIA, and SAVEE) under five Signal-to-Noise Ratios (SNR), to solve the problem of data scarcity. Finally, an evaluation index EAI is defined to comprehensively analyze the performance of the model under different data division namely EIRD, EDRD, EICV, and EDCV. Compared to the previous research results, the model I-CapsNet has competitive performance on the corresponding augmented datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Speech recognition

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Anonymous Verifiable Sealed Quotation Auction Based on Blockchain

Anonymous Verifiable Sealed Quotation Auction Based on Block...

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作者： Ke Wei Yulong Dang The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University

With the development of blockchain technology, the existing online auction technology is developing from the trusted third party as auction intermediary to the disintermediation to the third party. Aiming at the problems existing in the existing sealed bid auction scheme based on blockchain, such as the leakage of bidders' privacy information, illegal acquisition or tampering of bidders' bids in sealed auction, etc. Based on blockchain as the bottom layer, an auction scheme with decentralization, bid privacy,anonymous verifiability, tamper-proof and collusion-proof is constructed. ASVChain uses encryption mechanism to encrypt each bidder's offer to ensure the privacy of the bid,and uses ring signature technology to sign the encrypted offer, providing safe and verifiable steps while ensuring that each bidder's personal information is not leaked, and preventing illegal tampering while resisting collusion. The security analysis shows the advantages of this scheme compared with the existing scheme. The experiment analyzes and tests the influence of the number of bidders on the bidding time, and the results show that the time consumption of the proposed auction scheme meets the daily auction demand.

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Fusion recommendation algorithm based on DeepWalk and Item-Based collaborative filtering 3

Fusion recommendation algorithm based on DeepWalk and Item-B...

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3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

作者： Yu, Yicheng Li, Rongbin Yin, Jun Qinghai Normal University School of Computing Xi'Ning China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xi'Ning China

ISBN: (纸本)9798350320404

Collaborative filtering techniques have been applied to personalised recommendation systems in recommender systems. However, with the gradual increase in the number of platform users and goods, the user rating data on the entire project matrix is extremely sparse, resulting in the inaccuracy of the prediction results. DeepWalk, on the other hand, still performs well when the data is sparse. Therefore, DeepWalk is introduced into collaborative filtering, and a fusion recommendation algorithm based on DeepWalk and collaborative filtering is proposed to reduce the impact of cold start and sparsity on the recommendation results. Experiments show that the proposed fusion recommendation algorithm has higher recommendation accuracy when the data is sparse compared with other traditional algorithms, which improves the recommendation quality to a certain extent. © 2022 IEEE.

关键词： Collaborative filtering

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Construction of a Three-Layer Directed Network Model with Multimodal Characteristics

Construction of a Three-Layer Directed Network Model with Mu...

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2021 International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2021

作者： Zhang, Na Xiao, Yuzhi Zhao, Haixing Tang, Cunyang Cui, Baoyang College of Computer Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781643682549

The multi-layer directed network model focuses on portraying the directionality, diversity and difference of the edges in the network. It is also one of the powerful tools for analyzing the complexity of the network system and the heterogeneous interaction characteristics between network layers. This paper constructs three three-layer directed network models combined with traditional complex network theory and analyzes their degree distribution characteristics. By controlling the out-degree and in-degree of the middle-level nodes, a network structure with multi-peak characteristics is configured. Combined with numerical simulation, it is concluded that the number of links between the layers of the directed network makes the network characterize the characteristics of unimodal, bimodal, and trimodal;the combination of the optimal mechanism and the changes in the number of inter-layer links, the network characteristics show the coexistence of power law and unimodal. The research results of this paper have practical value for the analysis of network generation mechanism and correlation law by using multi-layer directed network theory in the era of big data. © 2022 The authors and IOS Press.

关键词： Network layers

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

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