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Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

computer Systems science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

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A Review of Intrusion Detection for Internet of Things Using Machine Learning

A Review of Intrusion Detection for Internet of Things Using...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Paul, Ekwueme Chika Amrita Sharda School of Engineering & Technology Sharda University Computer Science & Engineering UP Greater Noida India Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University UP Greater Noida India

ISBN: (纸本)9798331542344

As Internet of Things (IoT) networks proliferate across consumer and industrial sectors, safeguarding these interconnected cyber-physical systems is imperative. IoT ecosystems present abundant targets for malicious intrusions including denial-of-service, man-in-the-middle, crypto and access attacks due to their scale and dynamic topology. Such intrusions can severely disrupt sensing, monitoring and control functions, enabling catastrophic cascading failures. Consequently, IoT platforms require intelligent intrusion detection system (IDS) to identify anomalous behaviors and known attack patterns. Conventional signature-based detection relying on expert rules proves inadequate against evolving attacks. In contrast, data-driven machine learning (ML) algorithms offer versatile capabilities for securing IoT by modeling normal vs anomalous traffic and information flows based on supervised, semi-supervised or unsupervised learning. Critical analysis reveals ML-driven intrusion detection frameworks achieve high accuracy in controlled settings. However, challenges related to computational burdens, adversarial attacks, concept drift and lack of model standardization need resolution before widespread field deployment. As IoT ecosystems continue proliferating amidst escalating threats, our systematic review helps identify limitations and promising directions for future research on ML-powered IDSs. This paper presents a comprehensive analysis of adopting ML techniques like neural networks, random forest, support vector machines, ensemble models and deep learning architectures to engineer anomaly detection and misuse detection modules tailored for IoT environments. © 2024 IEEE.

关键词： Network intrusion

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UniSaT: Unified-Objective Belief Model and Planner to Search for and Track Multiple Objects

UniSaT: Unified-Objective Belief Model and Planner to Search...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Santos, Leonardo Moon, Brady Scherer, Sebastian Nguyen, Hoa Van School of Engineering Federal University of Minas Gerais MG Belo Horizonte Brazil Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Curtin University BentleyWA6102 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce UniSaT (Unified Search and Track), a novel unifiedobjective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment, presenting both qualitative results and quantitative improvements over a multi-objective method. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Object tracking

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ACSarF:a DRL-based adaptive consortium blockchain sharding framework for supply chain finance

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Digital Communications and Networks 2025年第1期11卷 26-34页

作者： Shijing Hu Junxiong Lin Xin Du Wenbin Huang Zhihui Lu Qiang Duan Jie Wu School of Computer Science Fudan UniversityShanghai 200438China Shanghai Blockchain Engineering Research Center Fudan UniversityShanghai 200438China Information Sciences and Technology Department Pennsylvania State UniversityAbingtonPA 19001USA

Blockchain technologies have been used to facilitate Web 3.0 and FinTech ***,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain ***,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based *** address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning *** proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF ***,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF *** evaluate the proposed framework,we conducted extensive experiments in a typical SCF *** obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.

关键词： Web 3.0 Consortium blockchain Blockchain sharding Supply chain finance FinTech regulation

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Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling 24

Resisting Over-Smoothing in Graph Neural Networks via Dual-D...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Shen, Wei Ye, Mang Huang, Wenke National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Graph Neural Networks (GNNs) are widely employed to derive meaningful node representations from graphs. Despite their success, deep GNNs frequently grapple with the oversmoothing issue, where node representations become highly indistinguishable due to repeated aggregations. In this work, we consider the oversmoothing issue from two aspects of the node embedding space: dimension and instance. Specifically, while existing methods primarily concentrate on instance-level node relations to mitigate oversmoothing, we propose to mitigate oversmoothing at dimension level. We reveal the heightened information redundancy between dimensions which diminishes information diversity and impairs node differentiation in GNNs. Motivated by this insight, we propose the Dimension-Level Decoupling (DLD) to reduce dimension redundancy, enhancing dimensional-level node differentiation. Besides, at the instance level, the neglect of class differences leads to vague classification boundaries. Hence, we introduce the Instance-Level Class-Difference Decoupling (ICDD) that repels inter-class nodes and attracts intra-class nodes, improving the instance-level node discrimination with clear classification boundaries. Additionally, we introduce a novel evaluation metric that considers the impact of class differences on node distances, facilitating precise oversmoothing measurement. Extensive experiments demonstrate the effectiveness of our method Dual-Dimensional Class-Difference Decoupling (DDCD) across diverse scenarios. © 2024 ACM.

关键词： Graph neural networks

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Empowering Visible-Infrared Person Re-Identification with Large Foundation Models 38

Empowering Visible-Infrared Person Re-Identification with La...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Zhangyi Yang, Bin Ye, Mang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Visible-Infrared Person Re-identification (VI-ReID) is a challenging cross-modal retrieval task due to significant modality differences, primarily resulting from the absence of color information in the infrared modality. The development of large foundation models like Large Language Models (LLMs) and Vision Language Models (VLMs) motivates us to explore a feasible solution to empower VI-ReID with off-the-shelf large foundation models. To this end, we propose a novel Text-enhanced VI-ReID framework driven by Large Foundation Models (TVI-LFM). The core idea is to enrich the representation of the infrared modality with textual descriptions automatically generated by VLMs. Specifically, we incorporate a pre-trained VLM to extract textual features from texts generated by VLM and augmented by LLM, and incrementally fine-tune the text encoder to minimize the domain gap between generated texts and original visual modalities. Meanwhile, to enhance the infrared modality with extracted textual representations, we leverage modality alignment capabilities of VLMs and VLM-generated feature-level filters. This enables the text model to learn complementary features from the infrared modality, ensuring the semantic structural consistency between the fusion modality and the visible modality. Furthermore, we introduce modality joint learning to align features across all modalities, ensuring that textual features maintain stable semantic representation of overall pedestrian appearance during complementary information learning. Additionally, a modality ensemble retrieval strategy is proposed to leverage complementary strengths of each query modality to improve retrieval effectiveness and robustness. Extensive experiments on three expanded VI-ReID datasets demonstrate that our method significantly improves the retrieval performance, paving the way for the utilization of large foundation models in downstream multi-modal retrieval tasks. © 2024 Neural information processing systems foundat

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A novel image encryption algorithm based on new one-dimensional chaos and DNA coding

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Multimedia Tools and Applications 2024年第36期83卷 84275-84297页

作者： Feng, Sijia Zhao, Maochang Liu, Zhaobin Li, Yuanyu Tianjin public emergency early warning information release center Tianjin Meteorology Information Center Tianjin300074 China School of Information Science & amp Technology Dalian Maritime University Dalian116026 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

This paper introduces a new one-dimensional chaotic system and a new image encryption algorithm. Firstly, the new chaotic system is analyzed. The bifurcation diagram and Lyapunov exponent show that the system has strong chaotic characteristics and is suitable for the field of image encryption. The chaotic sequence generated by the system is used in image encryption, scrambled according to its sequence value, and several sequences required for DNA coding operation are generated. The encrypted image is obtained by encoding, decoding, and diffusion operation according to the sequence. The initial values and parameters of system are generated by Hash algorithm based on plain image, so it has strong sensitivity and can effectively resist attacks such as selective plaintext attack. Experimental results show that the algorithm has high security and robustness, and can resist common attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Chaotic systems

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

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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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Harmonizing space–time dynamics for precision in human action recognition

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Multimedia Tools and Applications 2025年第5期84卷 2805-2832页

作者： Majid, Abdul Wang, Yulin Ullah, Anwar Naiz, Fahim Zarar, Muhammad School of Computer Science Wuhan University Hubei Wuhan430072 China National Engineering Research Center for E-Leaning Central China Normal University Hubei Wuhan430072 China

Human action recognition (HAR) plays a vital role in various fields, including surveillance, healthcare, and human–computer interaction. Recognizing multi-actor actions in crowded scenarios poses a significant challenge due to the complex dynamics and interactions among individuals. Existing methods often fail to effectively capture both spatial and temporal dependencies in video frames, leading to inaccuracies in recognizing diverse action classes under real-world conditions. In this paper, we introduce a novel framework called Video Capsule Net-Multi Actor (VCN-MA), designed to enhance the precision of HAR by harmonizing space–time dynamics. The VCN-MA architecture incorporates a deep neural network combining convolutional and recurrent layers to capture spatial and temporal features. Additionally, we leverage pre-trained embeddings and attention mechanisms to focus on critical features during action sequences. The proposed model utilizes capsule networks with dynamic routing to represent complex hierarchical spatial–temporal relationships, further refining action recognition accuracy. We evaluate our approach using the MAMA dataset, which includes 35 diverse action classes under varied real-world conditions such as different viewpoints, occlusions, and lighting variations. The VCN-MA model achieves an overall accuracy of 85% on the test set, outperforming existing state-of-the-art methods. Visualizations, including confusion matrices and ROC curves, along with metrics like the Matthews Correlation Coefficient (MCC), provide a comprehensive analysis of the model's performance across different action classes. These results demonstrate the potential applicability of VCN-MA in practical scenarios, including video surveillance, human–computer interaction, and healthcare applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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