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2022 International Conference on networking and network Applications, NaNA 2022

作者： Zhou, Yongjie Xiong, Jinbo Bi, Renwan Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network System Information Security College of Computer and Cyberspace Security Fujian Normal University FJ Fuzhou350117 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead. © 2022 IEEE.

关键词： Autonomous vehicles

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Design of power supply system for portable aviation inspection equipment 3

Design of power supply system for portable aviation inspecti...

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2023 3rd International Conference on Measurement Control and Instrumentation, MCAI 2023

作者： Yang, Yang Ai, Hao Li, Shibo Xiao, Guokai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Given that airborne electronic equipment may have a variety of wiring methods and may encounter a set of systems using multiple power supplies, it is necessary to take some measures on the circuit to ensure the normal work of the power supply and the post-circuit. A portable aviation detection equipment power supply system is designed in this paper. The system supports simultaneous access of two power supplies to identify and judge the input power supply and switch between the battery and the external power supply, effectively avoiding the security risks caused by battery backflow current. The battery power is monitored in real-time through the battery power detection circuit and will be reminded when the battery power is too low. Since the circuit uses a MOS tube to control the switching of the power supply, there is almost no voltage drop loss. In addition, the EMC protection circuit is set up at the power supply side to further improve the safety and reliability of the system. © Published under licence by IOP Publishing Ltd.

关键词： Oscillators (electronic)

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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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Dual-View Self-supervised Co-training for Knowledge Graph Recommendation 28th

Dual-View Self-supervised Co-training for Knowledge Graph R...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Ruoyi Ma, Huifang Li, Qingfeng Wang, Yike Li, Zhixin College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9783031306716

Knowledge Graph Recommendation (KGR), which aims to incorporate Knowledge Graphs (KGs) as auxiliary information into recommender systems and effectively improve model performance, has attracted considerable interest. Currently, KGR community has focused on designing Graph Neural networks (GNNs)-based end-to-end KGR models. Unfortunately, existing GNNs-based KGR models are focused on extracting high-order attributes (knowledge) but suffer from restrictions in several vital aspects, such as 1) neglect of finer-grained feature interaction information via GNNs and 2) lack of adequate supervised signals, leading to undesirable performance. To tackle these gaps, we propose a novel Dual-view Self-supervised Co-training for Knowledge Graph Recommendation (DSCKG). We consider two different views, covering user-item collaborative view and KGs structural view. Precisely, for the collaborative view, we first extract high-order collaborative user/item representations with GNNs. Next, we impose a discrepancy regularization term to augment the self-discrimination of the user/item representations. As for the structural view, we initially utilize GNNs to extract high-order features. Next, we utilize novel Dual-core Convolutional Neural networks to extract bit- and vector-level finer-grained feature interaction signals. DSCKG hence performs a high-quality self-supervised co-training paradigm across dual views, improving the node representation learning capability. Experimental results demonstrate DSCKG achieves remarkable improvements over SOTA methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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CurMIM: Curriculum Masked Image Modeling

CurMIM: Curriculum Masked Image Modeling

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked Image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked Image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 IEEE.

关键词： Curriculum Learning Image Representation Masked Image Modeling Visual Pre-training

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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BIFLC: A Blockchain and IPFS-Based Multi-Consensus Federated Learning Framework 26

BIFLC: A Blockchain and IPFS-Based Multi-Consensus Federated...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Li, Jing Sun, Jufeng Li, Fengyong Yang, Xingkai Wang, Yuwei Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China

ISBN: (纸本)9798350331684

Federated learning is an efficient technology that implements distributed model training among multiple data sources with local data, and can realize data privacy protection and data sharing ***, existing federated learning models may involve a large number of external attacks that can reconstruct the original training data using the acquired model, resulting in possible global model or user privacy data attacks. To address the above problem, we propose a new decentralized multiconsensus federated learning model by combining blockchain and interplanetary file system (IPFS), named as BIFLC. To be specific, we firstly design an on-chain consensus process based on a blockchain hybrid consensus mechanism by introducing a proof- of-work (PoW) and a proof-of-stake (PoS) mechanism, which can ensure the integrity of the on-chain consensus process and provide a chained data hash index for data. Furthermore, we introduce the interplanetary file system to reduce the cost of storing data on the chain and employ its distributed content delivery mechanism to save bandwidth. Extensive experiments demonstrate that our proposed scheme has higher accuracy and lower IPFS transmission time. © 2023 IEEE.

关键词： Blockchain

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Intra- and Inter-behavior Contrastive Learning for Multi-behavior Recommendation 28th

Intra- and Inter-behavior Contrastive Learning for Multi-be...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Li, Qingfeng Ma, Huifang Zhang, Ruoyi Jin, Wangyu Li, Zhixin College of Computer Science and Engineering Northwest Normal University Gansu Lanzhou China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9783031306716

Multi-behavior recommendation (MBR) aims to improve the prediction of target behavior by exploiting multi-typed auxiliary behaviors. However, most MBR suffers from data sparsity in real-world scenarios and thus performs mediocrely. In this paper, we propose a novel Intra- and Inter-behavior Contrastive Learning (IICL) framework to exploit contrastive learning to enhance MBR with two key challenges to be addressed: i). Difficult to learn reliable representations under different behaviors;ii). Sparse supervised signals under target behavior. For the first challenge, we devise an intra-behavior contrastive learning objective, which enhances the representation learning by incorporating potential neighbors into the contrastive learning from the graph topological space and the semantic space, respectively. As for the second challenge, we design an inter-behavior contrastive learning objective, which has the benefit of capturing commonalities between different behaviors and integrating them into the target behavior to alleviate the sparse supervised signal problem. In addition, we also propose an adaptive weight network to customize the integration of all losses efficiently. Extensive experiments on three real-world benchmark datasets show that our proposed method IICL is significantly superior to various state-of-the-art recommendation methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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MISP: An Efficient Quantum-resistant Misbehavior Preventing Scheme with Self-enforcement for Vehicle-to-Everything

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Ying He, Debiao Bao, Zijian Wang, Huaqun Luo, Min Wuhan University School of Cyber Science and Engineering Wuhan430072 China Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Nanjing University of Posts and Telecommunications College of Computer Nanjing210023 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network & Information System Security Jinan250014 China

In the Vehicle-to-Everything (V2X) communication system, the presence of ambiguous warnings significantly increases the risk of severe accidents, posing a substantial threat to the safety of autonomous driving. It is crucial to detect such confusing warnings to avoid danger. Existing solutions to address this issue heavily rely on trust entities or are constructed based on number theory assumptions, leading to low efficiency and vulnerability to quantum attacks. In this paper, we leverage the double authentication-preventing signature scheme (DAPS) to present a revocable identity-based double-authentication preventing signature scheme (RIDAPS) and provides an instantiation from lattice. Furthermore, we propose a post-quantum secure misbehavior preventing scheme (Misp) based on our RIDAPS scheme. We give a detailed proof in the random oracle model (ROM) to demonstrate that our contribution achieves security requirements. Additionally, the efficiency evaluation results demonstrate that our scheme is suitable to be applied in V2X. © 2004-2012 IEEE.

关键词： Authentication

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Fairness of Large Music Models: From a Culturally Diverse Perspective 9

Fairness of Large Music Models: From a Culturally Diverse Pe...

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

作者： Wang, Qinyuan Gu, Bruce Zhang, He Li, Yunfeng University of Sydney School of Music NSW2008 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China CNPIEC KEXIN Ltd. Beijing100020 China

ISBN: (纸本)9798350391367

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions. © 2024 IEEE.

关键词： Adversarial machine learning

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