检索结果-内蒙古大学图书馆

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

来源：评论

学校读者我要写书评

暂无评论

Multi-dimensional Security Range Query for Industrial IoT

引用

computers, Materials & Continua 2022年第7期72卷 157-179页

作者： Abdallah Abdallah Ayman A.Aly Bassem F.Felemban Imran Khan Ki-Il Kim School of Engineering Technology Al Hussein Technical University(HTU)Amman11831Jordan Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

The Internet of Things(IoT)has allowed for significant advancements in applications not only in the home,business,and environment,but also in factory *** Internet of Things(IIoT)brings all of the benefits of the IoT to industrial contexts,allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and *** expansion of the IoT has been set by serious security threats and obstacles,and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control.A privacypreserving multi-dimensional secure query technique for fog-enhanced IIoT was proposed in light of the fact that most existing range query schemes for fog-enhanced IoT cannot provide both multi-dimensional query and privacy *** query matrix was then decomposed using auxiliary vectors,and the auxiliary vectorwas then processed usingBGNhomomorphic encryption to create a query ***,the query trapdoor may be matched to its sensor data using the homomorphic computation used by an IoT device *** the application of particular auxiliary vectors,the spatial complexity might be efficiently *** homomorphic encryption property might ensure the security of sensor data and safeguard the privacy of the user’s inquiry *** results of the experiments reveal that the computing and communication expenses are modest.

关键词： Internet of things data security ciphertext privacy encryption

来源：评论

学校读者我要写书评

暂无评论

COMPLEX INDOOR NOISE MODELLING CASE STUDY 29

COMPLEX INDOOR NOISE MODELLING CASE STUDY

引用

29th International Congress on Sound and Vibration, ICSV 2023

作者： Omar, Odeh Maria, Bite Dénes, Kocsis Doctoral School of Informatics University of Debrecen Hungary Vibrocomp Ltd. Hungary Department of Environmental Engineering Faculty of Engineering University of Debrecen Hungary

ISBN: (纸本)9788011034238

Noise modelling plays a very important role in predicting noise levels during the design phase to ensure that new constructions will not have adverse effects on the environment and humans' health by applying various mitigation measures to keep noise levels below the limit value. In this work, the Public Address and General Alarm (PAGA) system of a fire station building in Doha, Qatar was evaluated to ensure full coverage of speakers and maintain speech intelligibility in all premises of the building. For that, an indoor noise modelling study followed by mitigation measures was performed using SounPLAN 9.0. Two main parameters were in focus: total noise (LAeq) for the speakers' coverage and the Speech Transmission Index (STI), for the speech intelligibility. Generally, STI ratings over 0.45 are sufficient (reaching at least "fair" level) and LAeq with minimum 15 dB higher than the background noise satisfies the audibility. The complexity in this work appeared when the clarity of the sound could not be achieved in all areas without installing extra speakers, taking into account that the maximum allowed noise in terms of HSE Occupational Noise Level (85 dB(A)) was not to be exceeded on any of the premises. Several mitigation measures were examined, and the most applicable option was recommended taking into consideration workers' health, availability of speakers in the market, and cost efficiency. The solution for this case study with the most significant challenges is presented in this work. © 2023 Proceedings of the International Congress on Sound and Vibration. All rights reserved.

关键词： Speech intelligibility

来源：评论

学校读者我要写书评

暂无评论

Microstructure Estimation Using Synergistic Dual-path Hybrid Network

Microstructure Estimation Using Synergistic Dual-path Hybrid...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Cao, Jian Yang, Junqing Wang, Siqi Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350386226

White matter microstructure plays a pivotal role in the diagnosis and study of brain disorders. Deep learning-based estimation of white matter microstructural indices from diffusion MRI (dMRI) data has gained increasing research attention in recent years. To conduct effective learning in the heterogeneous space (i.e., x-space and q-space) of dMRI data, hybrid neural networks were proposed and have shown great potential. To this end, we propose a new hybrid neural network, called synergistic dual-path hybrid convolutional neural network (SDH-Net), for more effective microstructure estimation. In our SDH-Net, we propose a dual-path architecture that takes bidirectional asymmetric learning in x-space and q-space to enhance feature representation in heterogeneous domains. Firstly, 3D patches are extracted from each diffusion gradient as vertices, and then a graph is constructed based on the correlation between diffusion angles. In the x-q learning branch, an effective representation is embedded in x-space to enhance learning in q-space, while vice versa in the q-x learning branch. Extensive experiments on data from the human connectome project demonstrate that our SDH-Net outperforms the existing state-of-the-art models. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Big Data Security through Privacy - Preserving Data Mining (PPDM): A Decentralization Approach 2

Big Data Security through Privacy - Preserving Data Mining (...

引用

2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Josphineleela, R. Kaliapp, S. Natrayan, L. Garg, Ashish Panimalar Engineering College Department of Computer Science Engineering Chennai600123 India Velammal Institute of Technology Department of Mechanical Engineering Chennai601204 India Saveetha School of Engineering Department of Mechanical Engineering Chennai602107 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350346640

The increasing familiarity and improvements in data mining technologies bring serious security threats to the data of individuals. An emerging technology that provides better security and data security is PPDM: Privacy-Preserving Data Mining. The fundamental tenet of PPDM is to alter the data in such a manner that data mining techniques may be carried out successfully without affecting the privacy of sensitive data included in the data. The research article is focused on reducing the security and privacy risk that is created by data mining operations. During the process of data collection, Information processing, and publication of data there may be much security breached in the processing of the data in the data mining process. This paper focuses on the wide problems and is concerned with data mining, and finding a solution to ensure security and access through the data by diving the centralized data into various segments to ensure security by privacy-preserving methods. Through the process of finding various user interfaces related to data processing and issues related to the security of private information, this article will also show the vital role of PPDM in data mining and security. © 2023 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

A Multi-objective Smart Grid Optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency 3rd

A Multi-objective Smart Grid Optimization: Integrating Swarm...

引用

3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Ammasai, Viswanathan Katbar, Nek Muhammad Kothalanka, Amarendra Vandadi, Venkata Ramana Udhaya Shankar Latha, Sindhu Sharma, Dilip Kumar Sengan, Sudhakar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering KSRM College of Engineering Andhra Pradesh Kadapa516003 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli62745 India

ISBN: (纸本)9789819783540

The realm of Smart Grid Systems (SGS) has witnessed unprecedented progress, driven by increasing energy demands and the integration of Renewable Energy Sources (RES). While these grids promise enhanced efficiency and sustainability, their involved and dynamic nature poses significant optimization challenges. Existing solutions, although innovative, often overlook the intricate interplay between diverse factors, leaving room for potential improvements in optimization approaches. Recognizing this gap, the current study delves into developing a novel method that integrates Ant Colony Optimization (ACO) with domain-specific heuristics. By focusing on analytical sections like Load Forecasting, Renewable Energy Integration, Peak Shaving, and Grid Resilience, this method attempts to bridge the divide that existing approaches miss. The proposed method not only ensures optimal energy distribution but also tailors the solution to address the unique features and requirements of modern SGS. A more reliable, effective, and Renewable Energy (RE) future is within reach according to the outcomes of this investigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

来源：评论

学校读者我要写书评

暂无评论

Verifying or Clarifying? User Preferences for Mobile Crowdsourcing in Response to Seemingly Inconsistent Sensor Data

引用

Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-35页

作者： Chiang, You-Hsuan Hsu, Je-Wei Chiu, Hsin-Lun Liu, Chung-En Huang, Tzu-Yu Chang, Yung-Ju KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

In the realm of smart cities, sensor technologies play a pivotal role in monitoring urban facilities and environments, providing real-time, site-specific information to residents. However, discrepancies often arise in sensor data due to variances in granularity, abstraction, and scope, which can foster uncertainty regarding the actual conditions on-site. This study explores whether, under these circumstances, individuals prefer on-site mobile crowds for verification purposes or for the provision of supplementary contextual information to aid in decision-making. Conducting an online study with 100 participants from Taiwan, who engaged in a think-aloud process while utilizing smart city sensor data for decision-making, our findings indicate that participants more often (54%) preferred seeking verification over supplementary contextual information (46%). Both pre-existing expectations and the sense of task urgency affected participants’ choices between verification and supplementary contextual information. However, we found that the driving factor for seeking supplementary contextual information was not sensor data deviating from pre-existing expectations, but rather the absence of such pre-existing expectations. Our qualitative data also uncovered five primary motivations and four factors influencing the choice of crowdsourced information. Overall, these findings contribute to our understanding of how people leverage on-site mobile crowds to supplement sensor data in the context of smart cities. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Algorithm-Driven Optimization of Cloud Computing Architectures for Superior Data Security and Efficiency 2

Algorithm-Driven Optimization of Cloud Computing Architectur...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pillai, N. Muthuvairavan Jesu Jayarin, P. David, D. Beulah Jeeva, K. Kumari, V Sheeja R.M.D Engineering College Department of Computer Science and Business Systems KavaraipettaiTamil Nadu Thiruvallur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Information Technology Tamil NaduThandalam Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil NaduThandalam Chennai India

ISBN: (纸本)9798350384277

This paper examines how algorithm-driven optimization transforms cloud computing systems, focusing on data security and efficiency. Following a thorough cloud environment study, algorithms were picked and adapted to solve specific difficulties. After integration into the architecture, these algorithms were tested in a controlled simulation environment. CPU utilization rose from 70% to 90%, memory from 65% to 85%, storage from 60% to 80%, and network bandwidth from 75% to 95%. These improvements improve computational and storage efficiency. Intrusion detection became AI-based, and encryption increased from 128-bit to 256-bit. Data breaches dropped from 5 to 1 per year, while security standards compliance increased from partial to full. These improvements strengthened cloud security. The system became more responsive from 200 to 100 ms. Server load distribution evened out, peak load handling improved, and user experience rating rose from 3.5/5 to 4.5/5. These enhancements highlighted optimized load *** consumption dropped 30%, lowering operational costs. High cooling requirements became moderate due to energy-efficient infrastructure. Sustainability goals were met by reducing annual carbon footprint by 30%. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Obstacle detection in railway tracks: vehicle autonomous system application

引用

International Journal of Vehicle Autonomous Systems 2025年第2期18卷 105-117页

作者： Sarkar, Swagata Baptist, Leo John Valarmathi, K. Degadwala, Sheshang Department of Artificial intelligence and Data Science Sri Sairam Engineering College West Tambaram Tamil Nadu Chennai India School of IT and Management BlueCrest University College Monrovia Liberia Department of Information Technology S.A. Engineering College Chennai Tamil Nadu India Department of Computer Engineering Sigma Institute of Engineering Vadodara Gujarat India

For the purpose of preventing accidents on railroads that might result in casualties and perhaps cause the train to be damaged or derailed, reliable obstacle detection could be of great assistance. The present system has drawbacks such as the generic object detectors do not have sufficient classes to account for all of the conceivable circumstances, and it is difficult to acquire data sets that include items on trains. This paper proposes the use of a shallow network for the purpose of learning railway segmentation from regular railway photos. The small receptive field of the network avoids overconfident predictions and enables the network to concentrate on the locally extremely distinct and recurring patterns of the train environment. Our system novelty gives the network the ability to maximise its effectiveness. The approach that we use is evaluated using a specialised data set that contains railways images that have been intentionally supplemented with obstructions. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Obstacle detectors

来源：评论

学校读者我要写书评

暂无评论

Identification and Containment of Intrusion Attacks in an IIoT Network based on Machine Learning and Blockchain Technology 2

Identification and Containment of Intrusion Attacks in an II...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Motamarri, Nikhita Satish, G. Uma, H.R. Barve, Amit Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of Computer Science and Engineering Vijayawada Andhra Pradesh 520001 India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Sheriguda Ibrahimpatnam Telangana Hyderabad501510 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya Karnataka 571448 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Department of Computer Science and Engineering Pune Maharashtra 412115 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamilnadu Chennai602105 India

ISBN: (纸本)9798350327533

The Blockchain technology is leveraged to establish a secure and tamper-resistant decentralized ledger, providing a transparent and immutable record of all transactions and communications within the IoT network. By implementing smart contracts, we ensure the integrity and authenticity of device interactions, mitigating the risk of unauthorized access and data manipulation. Complementing the Blockchain, Machine Learning (ML) algorithms are employed to analyze and identify patterns indicative of malicious activities. The proposed system employs anomaly detection models, trained on historical data, to continuously monitor and adapt to emerging threats. This dynamic threat detection mechanism enhances the ability of the IoT network to recognize and respond to sophisticated intrusion attempts in real-Time. Furthermore, the proposed framework incorporates a decentralized identity management system using Blockchain, ensuring that only authenticated and authorized devices can participate in the IoT network. This not only safeguards against unauthorized access but also protects the privacy and integrity of the data generated and exchanged by IoT devices. To validate the effectiveness of our approach, we conducted experiments in a simulated IoT environment, considering various attack scenarios. The results demonstrate a significant improvement in the overall security posture of the IoT network, with reduced false positives and swift identification of intrusions. In conclusion, the integration of Blockchain and ML techniques provides a robust and adaptive defense mechanism for IoT device networks. This holistic approach addresses the challenges of security, privacy, and data integrity, paving the way for a more resilient and trustworthy IoT ecosystem in the face of evolving cyber threats. © 2024 IEEE.

关键词： Blockchain Integrity Internet of Things (IoT) Intrusion Intrusion Detection System Machine Learning Security

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：