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Deniable Identity-Based Matchmaking Encryption for Anonymous Messaging

IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2197-2210页

作者： Cao, Yanmei Wei, Jianghong Huang, Xinyi Chen, Xiaofeng Xiang, Yang Xi'an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Artifcial Intelligence Thrust Information Hub Guangzhou511400 China Swinburne University of Technology School of Software and Electrical Engineering MelbourneVIC3122 Australia

Anonymous messaging system allows users to deliver messages without revealing the sending content and their identifiers, which has attracted ongoing concerns. However, to the best of our knowledge, all the existing solutions still fail to protect users' privacy under coercion. That is, the user's communication transcripts might be tracked and cached, and later coerced to reveal the underlying messages associated with the ciphertexts due to law enforcement reasons or for evil purposes. In addition, anonymity alone is not enough for some practical scenarios. For instance, a journalist just wants to communicate with those intended informants, and thereby gather information from anonymous but authenticated sources. In this paper, to address the above-mentioned issues, we introduce an authenticated and deniable anonymous messaging framework. Its core component is a new cryptographic primitive dubbed deniable identity-based matchmaking encryption (DIB-ME), which captures plausible deniability under coercion and mutual authentication without interactions simultaneously. We further present a concrete DIB-ME construction and prove its security in the random oracle model. The performance analysis indicates merits of the proposed DIB-ME scheme. We also implement the proposed DIB-ME construction and present extensive experiment results, as a proof of concept to demonstrate its soundness and practicability. © 2024 IEEE.

关键词： Anonymity

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UAV-Mounted Intelligent Reflecting Surface in Maritime Wireless Powered Communication Network

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China Communications 2025年第3期22卷 254-269页

作者： Liu Yuan Han Fengxia Zhao Shengjie School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan 250357China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China School of Software Engineering Tongji UniversityShanghai 201804China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of EducationShanghai 200003China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804China

Intelligent reflecting surface(IRS)assisted with the wireless powered communication network(WPCN)can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring *** this paper,we investigate a reliable communication structure where multiple buoys transmit data to a base station(BS)with the help of the unmanned aerial vehicle(UAV)-mounted IRS and harvest energy from the base station *** organically combine WPCN with maritime data collection scenario,a scheduling protocol that employs the time division multiple access(TDMA)is proposed to serve multiple buoys for uplink data ***,we compare the full-duplex(FD)and half-duplex(HD)mechanisms in the maritime data collection system to illustrate different performances under these two *** maximize the fair energy efficiency under the energy harvesting constraints,a joint optimization problem on user association,BS transmit power,UAV’s trajectory and IRS’s phase shift is *** solve the non-convex problem,the original problem is decoupled into several subproblems,and successive convex optimization and block coordinate descent(BCD)methods are employed obtain the near-optimal solutions *** results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

关键词： full-duplex IRS joint optimization TDMA UAV WPCN

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Multi-Modal Incomplete Brain Tumor Segmentation with Mamba 24

Multi-Modal Incomplete Brain Tumor Segmentation with Mamba

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Li, Yudan Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun School of Electrical Engineering University of South China Hunan Hengyang China School of Computing/Software University of South China Hunan Hengyang China

ISBN: (纸本)9798400717826

Accurately segmenting brain tumors from multimodal MRI sequences is a key prerequisite for brain tumor diagnosis, prognosis assessment, and surgical treatment. However, in practical applications, one or more modal data is often missing due to image corruption, different acquisition protocols, artifacts, contrast agent allergies, or cost considerations. To address the challenges of brain tumor segmentation under modality loss, this paper proposes an innovative tumor feature perception strategy. The core of this strategy is to introduce a Mamba-based Encoder (MBE) architecture, which effectively improves the feature expression ability of each modality encoder under limited data conditions. In view of the irregularity of tumor morphology, a Modulation and Demodulation Fusion Block (MDFB) is designed to accurately capture the semantic features of the tumor from the missing multimodal image data, providing strong guidance for the network to locate the tumor area. Experimental results on the widely used BraTS2020 dataset demonstrate the effectiveness of MMITS, and the brain tumor segmentation effect under various incomplete modalities is better than the state-of-the-art methods. © 2024 Copyright held by the owner/author(s).

关键词： Multicarrier modulation

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RPPFL: Robust and Privacy-Preserving Federated Learning via Trusted Execution Environments

RPPFL: Robust and Privacy-Preserving Federated Learning via ...

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

作者： Zhang, Xiaolei Chen, Zhaoyu Chen, Guangpu Feng, Xinyu Shen, Qingni Wu, Zhonghai School of Computer Science Peking University China School of Software and Microelectronics Peking University China National Engineering Research Center for Software Engineering Peking University China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University China

ISBN: (纸本)9798350368741

Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10×, with less than a 9% loss in model performance under poisoning attacks. © 2025 IEEE.

关键词： Data Obfuscation Federated Learning Privacy Preservation Robustness Trusted Execution Environments

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared Small Target Segmentation Semantic Injection Wavelet Transformation

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An Empirical Study on Meta Virtual Reality Applications: Security and Privacy Perspectives

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IEEE Transactions on software engineering 2025年第5期51卷 1437-1454页

作者： Guo, Hanyang Dai, Hong-Ning Luo, Xiapu Xu, Gengyang He, Fengliang Zheng, Zibin Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Sun Yat-sen University School of Software Engineering Zhuhai519082 China The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong

Virtual Reality (VR) has accelerated its prevalent adoption in emerging metaverse applications, but it is not a fundamentally new technology. On the one hand, most VR operating systems (OS) are based on off-the-shelf mobile OS (e.g., Android OS). As a result, VR apps also inevitably inherit privacy and security deficiencies from conventional mobile apps. On the other hand, in contrast to traditional mobile apps, VR apps can achieve an immersive experience via diverse VR devices, such as head-mounted displays, body sensors, and controllers. However, achieving this requires the extensive collection of privacy-sensitive human biometrics (e.g., hand-tracking and face-tracking data). Moreover, VR apps have been typically implemented by 3D gaming engines (e.g., Unity), which also contain intrinsic security vulnerabilities. Inappropriate use of these technologies may incur privacy leaks and security vulnerabilities although these issues have not received significant attention compared to the proliferation of diverse VR apps. In this paper, we develop a security and privacy assessment tool, namely the VR-SP detector for VR apps. The VR-SP detector has integrated program static analysis tools and privacy-policy analysis methods. Using the VR-SP detector, we conduct a comprehensive empirical study on 900 popular VR apps. We obtain the original apps from the popular SideQuest app store and extract Android PacKage (APK) files via the Meta Quest 2 device. We evaluate the security vulnerabilities and privacy data leaks of these VR apps through VR app analysis, taint analysis, privacy policy analysis, and user review analysis. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps. Moreover, our results also reveal conflicting representations in the privacy policies of these apps and inconsistencies of the actual data collection with the privacy-policy statements of the apps. Further, user reviews also indicate their privacy concerns about rele

关键词： Differential privacy

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Research on Authorization Verification Methods for Message Signatures Within Multi-Level Groups 1

Research on Authorization Verification Methods for Message S...

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1st International Conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Zhang, Hu Zhang, Jiarui Zhang, Han Undergraduate Software Engineering Class of 2021 Hefei University Hefei China Department of Information Engineering Yantai Institute of Technology Yantai China Beijing Academy of Blockchain and Edge Computing Beijing China

ISBN: (纸本)9781643685779

With the widespread adoption of digital signature,a variety of specialized digital signature methods have been developed. However, the need for authorization verification within hierarchical group structures has not garnered sufficient attention. This research has addressed this issue. Initially, the problem was delineated and formally articulated;subsequently, encryption and decryption algorithms that incorporate authorization sharing were designed, along with a description of the code;finally, the methods were validated through experimentation. The outcomes demonstrate that the proposed approach is reliable and stable, capable of effectively accommodating the authorization verification of signed messages within multi-tiered, large-scale group communications. This addresses the shortfall of current digital signature technologies, which struggle to authorize multiple verifiers within hierarchical group structures, by supplementing and refining a method for the authorization verification of signed messages among users in multi-level groups. © 2025 The Authors.

关键词： Authorization

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