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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

Digital Communications and networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou 350117China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog ***,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of *** address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain *** particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between ***,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain ***,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using ***,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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A new quantum key distribution resource allocation and routing optimization scheme

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Chinese Physics B 2024年第3期33卷 244-259页

作者：毕琳袁晓同吴炜杰林升熙 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130012China Key Laboratory of Network and Information Security in Jilin Province Changchun 130012China

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic ***,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical *** this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet ***,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot ***,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key ***,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key *** results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

关键词： quantum key distribution(QKD) resource allocation key storage routing algorithm

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Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8477-8496页

作者： Xu, Dongsheng Chen, Chuanming Jin, Qi Zheng, Ming Ni, Tianjiao Yu, Qingying School of Computer and Information Anhui Normal University Anhui Wuhu China Anhui Provincial Key Laboratory of Network and Information Security Anhui Wuhu China

Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods. © 2024 – IOS Press. All rights reserved.

关键词： Trajectories

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Weighted Attribute Based Conditional Proxy Re-Encryption in the Cloud

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computers, Materials & Continua 2025年第4期83卷 1399-1414页

作者： Xixi Yan Jing Zhang Pengyu Cheng Henan Key Laboratory of Network Cryptography Technology Network Information Security and Cryptography LaboratorySchool of SoftwareHenan Polytechnic UniversityJiaozuo454003China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights *** introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced *** the same time,a weighted tree structure is constructed to simplify the expression of access structure *** conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal *** scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.

关键词： Cloud service conditional proxy re-encryption user revocation weighted attribute

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High-Capacity Image Hiding via Compressible Invertible Neural network 24th

High-Capacity Image Hiding via Compressible Invertible Neur...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Changguang Shi, Haoyi Li, Qingru Zhao, Dongmei Wang, Fangwei College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China

ISBN: (纸本)9789819615278

Image steganography involves the concealment of confidential information within images, rendering it undetectable to unauthorized observers, and subsequently retrieving the hidden information following secure transmission. Numerous investigations into image steganography employ invertible neural networks. Nevertheless, the intricate architecture of these models poses significant challenges to both their training and inference processes. Consequently, this paper proposes a high-capacity image steganography scheme utilizing compressible modules. We propose a multi-scale attention module that compresses the model structure through structural reparameterization post-training, thereby enhancing inference speed. Additionally, we employ a pre-trained image autoencoder to extract deep features from large-scale images, facilitating high-capacity steganography within invertible neural networks by concealing key features. Experimental results indicate that our approach offers superior image quality and model inference speed, while significantly enhancing security relative to existing methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Malicious Client Detection with Differential Privacy Constraints Based on Model Update Slicing and K-Means Clustering 6

Malicious Client Detection with Differential Privacy Constra...

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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Qi, Mengshi Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

ISBN: (纸本)9798331541750

Federated learning (FL) is a decentralized machine learning technique that protects data privacy by trainng models locally on devices and sharing only model updates instead of raw data. However, FL also faces multiple security challenges. Clientside poisoning attacks can compromise global model training by injecting malicious updates. Additionally, the server may infer private information from client model updates. Most existing studies address these two risks separately, lacking a systematic analysis and response to their combined threats. This paper proposes a method called Sliced-Clustering Guard (SCGuard), which slices gradients and performs clustering analysis on each segment, enabling the server to more accurately detect malicious updates and effectively differentiate malicious client updates with differential privacy (DP) protection. Experimental results demonstrate that SCGuard can mitigate poisoning attacks while effectively safeguarding clients' sensitive information. © 2024 IEEE.

关键词： Federated learning

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New results on quantum boomerang attacks

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Quantum information Processing 2023年第4期22卷 1-27页

作者： Zou, Hongkai Zou, Jian Luo, Yiyuan College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fuzhou China School of Information Sciences and Technology Huizhou University Huizhou China

At SAC 2021, Frixons et al. proposed quantum boomerang attacks that can effectively recover the keys of block ciphers in the quantum setting. Based on their work, we further consider how to quantize the generic boomerang attacks proposed by Biham et al. at FSE 2002, so as to obtain more generic quantum boomerang attacks. Similar to Frixons et al.’s work, we only consider quantum key recovery attacks in the single-key setting. With the help of some famous quantum algorithms, this paper presents two methods to convert the attacks of Biham et al. into some new quantum key recovery attacks. In order to proof our methods, we apply our new ideas to attack Serpent-256 and ARIA-196. To sum up, for Serpent-256, we give valid 9-round and 10-round quantum key recovery attacks respectively. The quantum time complexity of 9-round and 10-round of Serpent-256 is 2 115.43 and 2 126.6 respectively. Furthermore, we show a valid quantum key attack on 6-round ARIA-196 which has a time complexity of 2 89.8 with negligible memory. The time complexity of the above quantum attacks are better than the corresponding classical attacks and quantum generic key recovery attack via Grover’s algorithm. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Quantum theory

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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label distribution learning for scene text detection

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Frontiers of computer Science 2023年第6期17卷 5-12页

作者： Haoyu MA Ningning LU Junjun MEI Tao GUAN Yu ZHANG Xin GENG School of Computer Science and Engineering and the Key Lab of Computer Network and Information Integration(Ministry of Education)Southeast UniversityNanjing 211189China ZTE Corporation Nanjing 210012China

Recently,segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved ***,existing methods usually need complex post-processing stages to process ambiguous labels,i.e.,the labels of the pixels near the text boundary,which may belong to the text or *** this paper,we present a framework for segmentation-based scene text detection by learning from ambiguous *** use the label distribution learning method to process the label ambiguity of text annotation,which achieves a good performance without using additional post-processing *** on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection.

关键词： scene text detection multi-task learning label distribution learning

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Toward data privacy preservation with ciphertext update and key rotation for IoT

Toward data privacy preservation with ciphertext update and ...

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作者： Shen, Shuaiyong Yang, Yang Liu, Ximeng College of Mathematics and Computer Science Key Lab of Information Security of Network Systems Fuzhou University Fujian Fuzhou China

Fine-grained access control become a research spotlight in the scenario of the smart home with Internet-of-Things (IoTs). However, most of the existing works could only realize ciphertext outsourcing with an unchangeable encryption key in the cloud. That is because the trivial solution needs great communication overhead. To overcome this challenge, we propose an updatable encryption based on SM9 (SM9-UE) scheme for the IoT smart home scenario, where SM9 is an identity and cryptographic standard adopted by GM/T0044. SM9-UE realizes secure and lightweight ciphertext updating through using the token generated by the data owner, which is information independent with the plaintext. We give the formal security definition and prove it to be adaptive update indistinguishability (IND-UPD) secure. Theoretical comparison and analysis show that our scheme is efficient in computation and storage overhead. Experimental results also indicate that SM9-UE can be practically applied to IoT smart home. © 2021 John Wiley & Sons Ltd.

关键词： Internet of things

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