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Inductive Relation Prediction by Disentangled Subgraph Structure

Tsinghua science and technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse

Traceable AI-driven Avatars Using Multi-factors of Physical ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Kedi Yang Zhenyong Zhang Youliang Tian State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being *** this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user’s identity model combining the manipulator’s iris feature and the AI proxy’s public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven *** analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars.

关键词： Iris Protocols Metaverse Avatars Authentication public key Impersonation attacks Manipulators Security Artificial intelligence

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse

arXiv

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arXiv 2024年

作者： Yang, Kedi Zhang, Zhenyong Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being detected. In this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user’s identity model combining the manipulator’s iris feature and the AI proxy’s public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven avatars. Security analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars. © 2024, CC BY-NC-ND.

关键词： Authentication Protocol

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Slicing PBFT Consensus Algorithm Based on VRF

Slicing PBFT Consensus Algorithm Based on VRF

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IEEE International Conference on Blockchain (Blockchain)

作者： Pengyu Chen Yuling Chen Chaoyue Tan Yuxiang Yang Bo Li Jiachen Huang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University GuiYang China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Blockchain technology has become a key support for optimizing Consortium Blockchain processes and improving information sharing efficiency. However, existing consortium blockchain consensus algorithms often exhibit declining efficiency and insufficient stability when facing large-scale networks and frequent membership changes, constraining their application in complex Consortium Blockchain scenarios. To address these issues, this paper proposes a Practical Byzantine Fault Tolerance based on Verifiable Random Function (VRSPBFT). VRSPBFT uses the node's VRF proof value as the election basis, ensuring that more credible and contributive nodes are more likely to become leaders, avoiding malicious abuse. The model also designs a VRF-based instant node joining mechanism, allowing new nodes to quickly join the consensus process without waiting, effectively alleviating the consensus interruption problem caused by frequent changes and improving overall efficiency and continuity. The VRSPBFT model not only provides new technical support for building secure and reliable blockchain-based Consortium Blockchain systems, but also offers valuable references for further optimizing consortium blockchain consensus algorithms. This helps promote the application of blockchain in complex Consortium Blockchain scenarios and facilitate the transparency and efficiency of Consortium Blockchain management.

关键词： Fault tolerance Voting Fault tolerant systems Buildings Information sharing Consensus algorithm Stability analysis

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Traceable AI-driven Avatars Using Multi-factors of Physical World and Metaverse 23

Traceable AI-driven Avatars Using Multi-factors of Physical ...

引用

23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Yang, Kedi Zhang, Zhenyong Tian, Youliang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being *** this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user's identity model combining the manipulator's iris feature and the AI proxy's public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven *** analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars. © 2024 IEEE.

关键词： Authentication Protocol

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Private Comparison Based on Digital Decomposition for IoT Cloud Environments

Private Comparison Based on Digital Decomposition for IoT Cl...

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Cyber science and technology Congress (CyberSciTech), IEEE

作者： Zaidong Li Yuling Chen Yun Luo Xintao Pei Yingqi Qin State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

data privacy concerns are increasingly impacting Internet of Things (IoT) and artificial intelligence (AI) applications. Among them, protecting the privacy of the underlying data has been a challenging task. With the progress of homomorphic encryption (HE) performance, it provides a new possibility to solve this problem. However, traditional homomorphic encryption has poor support for some complex computational logic, such as comparison functions. In this paper, a homomorphic encrypted data comparison scheme based on digital decomposition is proposed to realize the comparison between homomorphic encrypted data, so that data users in the Internet of Things system can perform security comparison operations on encrypted data, and effectively protect the privacy of key data in the system. And implemented using the BGV scheme in the HElib library, experimental results have demonstrated the effectiveness of this approach.

关键词： data privacy Privacy Statistical analysis Machine learning Polynomials Libraries Internet of Things Matrix decomposition Logic Homomorphic encryption

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Research on Prediction Method of Auto Insurance Claim Amount Based on Graph Deep Learning 3

Research on Prediction Method of Auto Insurance Claim Amount...

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2023 3rd International Conference on big data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Lu, Jianguang Liu, Pengfei Xing, Bin Chongqing Industrial Big Data Innovation Center Co. Ltd. Chongqing400707 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang550025 China

ISBN: (纸本)9798400716478

The number of autos in China has surpassed 312 million, and automotive insurance is the most important part of property insurance. A win-win scenario for policyholders and property insurance firms may be achieved by precisely predicting the amount of auto insurance claims. This will also assist to measure premiums and successfully minimize auto insurance fraud. The existing approach of estimating the amount of auto insurance claims suffers from issues with a simple feature extraction method and a lack of focus on feature correlations, which leads to low prediction accuracy. This research suggests a strategy for forecasting the auto insurance claims amount based on graph deep learning to address these issues. The approach starts by automatically extracting features using convolutional neural networks, generates a feature connection graph, applies graph neural networks to learn and represent the feature relationship graph, and then properly forecasts the amount of auto insurance claims. The experimental results demonstrate that, when compared with Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), the approach described in this work decreases the root mean square error by 3.10% and 2.15%, and the mean absolute error by 3.98% and 1.14%, respectively. This article offers a fresh perspective on the estimation of vehicle insurance claims, as well as implications for other numerical regression prediction disciplines. © 2023 ACM.

关键词： Feature extraction

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Text-Image Scene Graph Fusion for Multimodal Named Entity Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2828-2839页

作者： Cheng, Jian Long, Kaifang Zhang, Shuang Zhang, Tian Ma, Lianbo Cheng, Shi Guo, Yinan State Key Laboratory for Intelligent Coal Mining and Strata Control Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing100013 China Northeastern University College of Software Shenyang110819 China Shaanxi Normal University College of Computer Science Xi'an710062 China China University of Mining and Technology Xuzhou221116 China

With the popularity and widespread use of social media platforms, such as Twitter and Facebook, massive amounts of text and image information posted by a variety of users have flooded these social media platforms. Thus, multimodal named entity recognition (MNER) has become a research hotspot for the task of extracting named entities from multimodal data. Empirically, the visual clues unrelated to text data may introduce uncertain or even negative impacts on the named entity recognition. The considerations of the relevance of multimodal data have been ignored in the previous studies. In this article, to effectively measure the relationship between text data and visual cues for improving the accuracy of named entities, we propose a text-image scene graph fusion (TISGF) approach with a text-image similarity assessment module (TISA) and a text-image fusion module (TIF) for MNER. Specifically, we first construct two (visual and textual) scene graphs to exploit the joint features of objects and relations in text and image and encode the two scene graphs separately using a specific encoder pair. In this way, we can obtain both object-level and relationship-level cross-modal features. Subsequently, TISA is used to compute the similarity of the image and text data and to determine the proportion of visual information that will be retained for fusion. Finally, we use TIF to achieve a unified multimodal representation of each word and predict the entity type using conditional random fields. Extensive experiment results on two public datasets demonstrate the effectiveness and competitiveness of our proposed method for the MNER task. © 2020 IEEE.

关键词： Semantics

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