检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

来源：评论

学校读者我要写书评

暂无评论

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service 19

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

引用

19th IEEE International Conference on e-science, e-science 2023

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

ISBN: (纸本)9798350322231

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** © 2023 IEEE.

关键词： Privacy-preserving techniques

来源：评论

学校读者我要写书评

暂无评论

SecBCS: a secure and privacy-preserving blockchain-based crowdsourcing system

引用

science China(Information sciences) 2020年第3期63卷 20-33页

作者： Chao LIN Debiao HE Sherali ZEADALLY Neeraj KUMAR Kim-Kwang Raymond CHOO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and Engineering Wuhan University State Key Laboratory of Cryptology College of Communication and Information University of Kentucky Department of Computer Science and Engineering Thapar University Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San Antonio

A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd management, conventional solutions, such as Upwork and Amazon Mechanical Turk, generally depend on a trusted third-party platform. There exist several potential security concerns(e.g., sensitive leakage, single point of failure and unfair judgment) in such a centralized paradigm. Hence, a recent trend in crowdsourcing is to leverage blockchain(a decentralized ledger technology) to address some of the existing limitations. A small number of blockchain-based crowdsourcing systems(BCSs) with incentive mechanisms have been proposed in the literature, but they are generally not designed with security in mind. Thus, we study the security and privacy requirements of a secure BCS and propose a concrete solution(i.e., SecBCS)with a prototype implementation based on JUICE.

关键词： crowdsourcing blockchain secure privacy-preserving smart contract

来源：评论

学校读者我要写书评

暂无评论

Model authentication hashing: Identifying pirated neural network models

引用

Expert Systems with Applications 2025年 283卷

作者： Xiong, Cheng Feng, Guorui Sun, Yunlong Qian, Zhenxing Yao, Heng Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Department of Tourism Fudan university Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of China Ministry of Culture and Tourism Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China

Nowadays, a great number of neural network models have been trained and applied in many fields. At the same time, the emergence of vast network models has already highlighted the security problems of intellectual property protection. High-value pre-trained models of original inventors may be pirated and used by illegal users without authorization. In order to solve this problem, we first give the definition of model differentiation, and then propose an effective robust hashing method based on model parameters for piracy identification in this paper. Through our model hash generation network, the parameters of original model are transformed into a one-way hash sequence with the fixed length, and the robustness and discrimination of the model hashes can be utilized to authenticate model parameters. Experimental results show that the accuracy rates of similar model detection and different model discrimination under different cases are almost all greater than 96.5%, and the comprehensive performance of proposed method is also the best one compared with other 3 SOTA methods. These results mean that the proposed method can be applied for identifying illegal model piracy effectively. © 2025 Elsevier Ltd

关键词： Terrorism

来源：评论

学校读者我要写书评

暂无评论

Backdoor-Empowered Regulable Privilege Authorization for Edge-Level Graph Learning in 6G Vehicular Networks

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Yang, Xiao Li, Gaolei Wu, Jun Zhou, Kai Li, Jianhua Yang, Wu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China The Hong Kong Polytechnic University Department of Computing Hong Kong Harbin Engineering University College of Computer Science and Technology Harbin China

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in flow prediction, route planning, and accident forecasting. Existing EGLS studies extremely stress absolute fairness and impartiality for all users, resultantly depriving its flexibility and humanity in addressing special circumstances and rendering the privilege authorization for exceptional responses challenging to achieve. To address this limitation, we present BackPriv, the first privilege authorization methodology enabling regulable privilege assignment or revocation in EGLS. Specifically, it is achieved through a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific privilege tokens to induce privilege outputs from the well-trained EGLS for authorized users. Moreover, related EGLS regulating strategies were proposed to dynamically realize the addition and revocation of user privileges by system retraining to guarantee privilege control in EGLS. Additionally, a Graph Mutual Information-based adaptive privilege token generation method is introduced to reduce the impact of privilege authorization on normal inputs and augment authorization functionality. Based on experiments with multiple real-world datasets, BackPriv demonstrates high success rates of privilege authorization under various scenarios ( 100%) and exerts minimal impact on normal task performance for regular inputs (mean accuracy drop 0.32%). © 1975-2011 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

arXiv

引用

arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

引用

IEEE Transactions on Mobile computing 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning

arXiv

引用

arXiv 2024年

作者： Huan, Ouwen Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Strong Privacy-Preserving and Efficient Fingerprint Authentication via Clustering

A Strong Privacy-Preserving and Efficient Fingerprint Authen...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Jingwei Liu Zihan Zhou Rong Sun Xiaojiang Du Mohsen Guizani Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi'an China State Key Laboratory of ISN Xidian University Xi'an China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken USA Department of Computer Science and Engineering Qatar University Doha Qatar

With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier bound to each individual, transmitting them directly to the cloud may bring about serious privacy disclosure risks. To guarantee users' biometric features, there are many solutions have been proposed. However, most of them neglect to protect identity security. In light of the challenges, this paper proposes a strong privacy-preserving fingerprint authentication via clustering. Besides safeguarding fingerprint features, the scheme also blurs the identities of users to enable anonymity of identity. Meanwhile, the authentication efficiency of the proposed scheme is improved by vector processing of fingerprints and fast retrieval of clustered identities. Furthermore, a dual-server matching architecture effectively reduces the communication overhead of the service provider. The security analysis and experimental results indicate that the proposed scheme provides strong privacy preservation while maintaining high efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

IEEE Networking Letters

引用

IEEE Networking Letters 2025年

作者： Li, Kai Liang, Yilei Yuan, Xin Ni, Wei Crowcroft, Jon Yuen, Chau Akan, Ozgur B. Group Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Real-Time and Embedded Computing Systems Research Centre CISTER Porto4249-015 Portugal The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FA United Kingdom Commonwealth Scientific and Industrial Research Organization CSIRO SydneyNSW2122 Australia The School of Computing Science and Engineering University of New South Wales Australia Nanyang Technological University SG 639798 Singapore Group Division of Electrical Engineering Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom The Center for neXt-generation Communications CXC Koç University Istanbul34450 Turkey

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. © 2019 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：