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Partial multi-label learning via label-specific feature corrections

Science China(information Sciences) 2025年第3期68卷 95-109页

作者： Jun-Yi HANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education

Partial multi-label learning（PML） allows learning from rich-semantic objects with inaccurate annotations, where a set of candidate labels are assigned to each training example but only some of them are valid. Existing approaches rely on disambiguation to tackle the PML problem, which aims to correct noisy candidate labels by recovering the ground-truth labeling information ahead of prediction model induction. However, this dominant strategy might be suboptimal as it usually needs extra assumptions that cannot be fully satisfied in real-world scenarios. Instead of label correction, we investigate another strategy to tackle the PML problem, where the potential ambiguity in PML data is eliminated by correcting instance features in a label-specific manner. Accordingly, a simple yet effective approach named PASE, i.e., partial multi-label learning via label-specific feature corrections, is proposed. Under a meta-learning framework, PASElearns to exert label-specific feature corrections so that potential ambiguity specific to each class label can be eliminated and the desired prediction model can be induced on these corrected instance features with the provided candidate labels. Comprehensive experiments on a wide range of synthetic and real-world data sets validate the effectiveness of the proposed approach.

关键词： machine learning multi-label learning partial multi-label learning label-specific features feature correction

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High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1139-1156页

作者： Kaili Qi Minqing Zhang Fuqiang Di Chao Jiang Key Laboratory of Network & Information Security under PAP Engineering University of PAPXi'an 710086China

Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding ***,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing ***,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding *** the data hiding phase,each encrypted image is sent to a data hider to embed the secret information *** r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be *** evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and *** last but the most important,it has higher embedding *** 512 ×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.

关键词： adaptive most significant bit(MSB)prediction password feedback secret sharing high embedding capacity reversible data hiding in encrypted images

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

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computers, Materials & Continua 2025年第1期82卷 645-674页

作者： Chunhui Li Xiaoying Wang Qingjie Zhang Jiaye Liang Aijing Zhang School of Information Engineering Institute of Disaster PreventionLangfang065201China Langfang Key Laboratory of Network Emergency Protection and Network Security Langfang065201China

Previous studies have shown that deep learning is very effective in detecting known ***,when facing unknown attacks,models such as Deep Neural networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low ***,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 *** hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 *** evaluation accuracy of the new dataset GINKS2023 created in this paper is *** to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

关键词： Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm

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Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement Data leakage Android

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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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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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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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Verifiable and Forward-Secure Multi-keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第13期12卷 23809-23822页

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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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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MarkNeRF:Watermarking for Neural Radiance Field

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computers, Materials & Continua 2024年第7期80卷 1235-1250页

作者： Lifeng Chen Jia Liu Wenquan Sun Weina Dong Xiaozhong Pan Cryptographic Engineering Department Institute of Cryptographic EngineeringEngineering University of PAPXi’an710086China Key Laboratory of Network and Information Security of PAP Xi’an710086China

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)*** employ an embedding network to integrate the watermark into the images within the training ***,theNeRFmodel is utilized for *** copyright verification,a secret image is generated by inputting a confidential viewpoint into *** this basis,design an extraction network to extract embedded watermark images fromconfidential *** the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s *** experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

关键词： Neural radiation field 3D watermark robustness black box

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