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Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks

Privacy-Secure and Decentralized Biometric Authentication Mo...

引用

2024 international conference on System, Computation, automation and Networking, ICSCAN 2024

作者： Mathivanan, P. Mahalakshmi, K. Anna University Faculty of Information and Communication Engineering Chennai India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331510022

A biometric identifier, in contrast to other identifiers used for authentication, is a quantitative evaluation of an individual's physical attributes that is successfully used to confirm or validate the identification. Because of its remarkable and consistent texture variation, iris recognition is thought to be the most dependable biometric recognition. Concerns about security, authentication, and identification are growing in importance across all domains as technology advances daily. High-security applications use these unique patterns for iris recognition. To increase the security of biometric applications without jeopardizing individual privacy, this research suggests a novel FL technique for privacy-preserving iris detection. Traditional biometric systems rely on centralised data storage and processing, which threatens data security and misuse. Federated Learning: decentralized model training that allows multiple federated clients (like mobile devices or edge servers) to collaboratively train a global model without sharing their raw iris data. We explore how this architecture can be applied to iris detection systems, maintaining accuracy while mitigating privacy risks. Our method ensures that iris features are learned locally on individual devices, and only encrypted model updates are shared with the central server. To strengthen privacy, we incorporate differential privacy and secure aggregation techniques to prevent data leakage during model training. Experimental results demonstrate the effectiveness of the proposed framework in maintaining high detection accuracy, low communication overhead, and robust privacy guarantees. © 2024 ieee.

关键词： Federated learning

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Comparative Analysis of Deep Learning Algorithms for Image Recognition in Medical Imaging 2

Comparative Analysis of Deep Learning Algorithms for Image R...

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2nd ieee international conference on Advances in Information Technology, ICAIT 2024

作者： Ashokkumar, S. Kumar, S. Magesh Sugumaran, D. Rajaraman, R. Saranya, N. Naga Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Tamil Nadu Chennai India

ISBN: (纸本)9798350383867

This research paper compares deep learning models through the imaging tasks of medical imaging. Modern-age architectures such as CNNs i.e., AlexNet, VGG, Resnet, Densensnet, and U-Net are the foundations of state of the art medical image analysis. They are applied to various tasks like detection of cancer, organ segmentation, and classification of diseases among others. Reports indicate that there are specific networks of neurons which perform optimization for specified applications in this sense of which the ResNet architecture is better than others in tumor detection and U-Net is better than SegNet in organ segmentation tasks. Leveraging the transfer learning techniques trained on the ImageNet, our approach to disease classification succeeded;in addition, we utilized ensemble methods that combined the predictions from multiple models, which were pretty effective. This research explores the choice and fine tuning deep learning strategies for medical image analysis and deals with a huge push to diversify imaging techniques diagnostics and treatment planning. ©2024 ieee.

关键词： Convolutional neural networks

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computer Vision Based Bangla Sign Language Recognition Using Transfer Learning 2

Computer Vision Based Bangla Sign Language Recognition Using...

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2nd international conference on Data science and Information System, ICDSIS 2024

作者： Sadik, Md Rezwane Sony, Rayhanul Islam Prova, Nuzhat Noor Islam Mahanandi, Y. Al Maruf, Abdullah Fahim, Sinhad Hossain Islam, Md.Shariful University Of South Dakota VermillionSD57069 United States Trine Univesity AngolaIN46703 United States Pace University Seidenberg School Of Csis One Pace Plaza New York CityNY10038 United States G Pulla Reddy Engineering College Computer Science And Engineering Kurnool India Bangladesh University Of Business & Technology Department Of Computer Science & Engineering Dhaka Mirpur Bangladesh Department Of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350381665

Human societies have relied on communication since ancient times, yet verbal communication poses significant challenges for deaf and hard-of-hearing individuals, necessitating reliance on sign language. Recent advancements, notably through deep learning models, have propelled research in this area. Acknowledging the need for further progress, particularly in minority languages like Bengali, our study aims to develop a method for image-based Bengali sign language detection. We constructed two independent convolutional neural network (CNN) models, InceptionV3 and Xception, leveraging data from diverse sources. Remarkably, the Inception V3 model achieved an accuracy of 97%, while the Xception model surpassed expectations with an accuracy of 99.50%. These results signify substantial progress, demonstrating the efficacy of deep learning architectures, especially the Xception model, in accurately interpreting Bengali sign language. Our study demonstrated how transfer learning, when combined with careful optimization, may yield remarkable outcomes in Bengali sign language recognition, which are further enhanced by data augmentation methods. © 2024 ieee.

关键词： Image classification

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Divide2Conquer (D2C): A Decentralized Approach Towards Overfitting Remediation in Deep Learning

Divide2Conquer (D2C): A Decentralized Approach Towards Overf...

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2024 ieee international conference on Big Data, BigData 2024

作者： Siddiqui, Md. Saiful Bari Islam, Md Mohaiminul Alam, Md. Golam Rabiul Brac University Department of Computer Science and Engineering Dhaka Bangladesh United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350362480

Overfitting remains a persistent challenge in deep learning. It is primarily attributed to data outliers, noise, and limited training set sizes. This paper presents Divide2Conquer (D2C), a novel technique designed to address this issue. D2C proposes partitioning the training data into multiple subsets and training separate identical models on them. To avoid overfitting on any specific subset, the trained parameters from these models are aggregated and averaged periodically throughout the training phase, enabling the model to learn from the entire dataset while mitigating the impact of individual outliers or noise. Empirical evaluations on multiple benchmark datasets across various deep learning tasks demonstrate that D2C effectively improves generalization performance, particularly for larger datasets. This study verifies D2C's ability to achieve significant performance gains both as a standalone technique and when used in conjunction with other overfitting reduction methods through a series of experiments, including analysis of decision boundaries, loss curves, and other performance metrics. It also provides valuable insights into the implementation and hyperparameter tuning of D2C. Our codes are publicly available at: https://***/Saiful185/Divide2Conquer. © 2024 ieee.

关键词： Deep learning

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Feature Decoupled of Deep Mutual Information Maximization 2

Feature Decoupled of Deep Mutual Information Maximization

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2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie Wang, Zifan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Guizhou Big Data Academy Guizhou University Guiyang China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

ISBN: (纸本)9798350308341

In deep learning, supervised learning techniques usually require a large amount of expensive labeled data to train the network, and the feature representations extracted by the model usually mix multiple attributes, resulting in feature representations that are difficult to decouple and are non-interpretable, which restricts the application and development of deep learning techniques, and for this reason, it is particularly important to study decoupled feature representation methods for unsupervised learning. Although the Learning deep representations by mutual information estimation and maximization (DIM) method achieves excellent results in unsupervised learning, the feature representations learned by the DIM method still suffer from the problem of difficult decoupling. decoupling problem. To address this problem, we minimize the mutual information between the intermediate layer feature representations learned by the hidden layer of the encoder during the encoder training process, so that the features learned by each filter are as uncorrelated as possible, thus realizing feature decoupling, and our method is called FP-DIM. Finally, the effectiveness of the FP-DIM method is verified on the CIFAR-10, STL-10, and Fashion-MNIST datasets. The experiments show that our proposed FP-DIM method is more significant for the learned decouplable middle layer feature representation. Finally, we also propose a reflection of future research for the FP-DIM method, aiming to provide a research idea and direction for solving unsupervised interpretable machine learning and to lay a solid theoretical and application foundation for machine learning fields such as image classification and migration learning. © 2023 ieee.

关键词： Unsupervised learning

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Neurocognitive assessment under various human-robot teaming environments 46

Neurocognitive assessment under various human-robot teaming ...

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46th Annual international conference of the ieee engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 ieee.

关键词： Man machine systems

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Intrusion Detection System Using Improved Gloden Jackal optimization for Security and Privacy in Internet of Things 2

Intrusion Detection System Using Improved Gloden Jackal opti...

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2nd international conference on Data science and Information System, ICDSIS 2024

作者： Bhagavatham, Naresh Kumar Al-Farouni, Mohammed Kumar, Komuravelly Sudheer Sundaram, S. Meenakshi Puneeth, R.P. Vignana Bharathi Institute Of Technology Dept Of Computer Science And Engineering Hyderabad India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Sr University School Of Computer Science & Artificial Intelligence Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai&ds Bengaluru India Department Of Computer Science And Engineering Karnataka India

ISBN: (纸本)9798350381665

Technology plays a primary role in rapid growth of service and identifying the quality of life. Recent technology such as Internet of Things (IoT) determines an impressive performance in the development of fast-forward. Intrusion Detection System (IDS) is employed as a lifeline to prevent attacks by categorizing activities as suspicious or normal. However, identifying the most relevant features from large data is challenging to effectively distinguish among suspicious or normal behavior. This research proposes the Improved Gloden Jackal optimization (IGJO) to select the appropriate features in IDS for security and privacy. GJO is improved by using tent mapping reverse learning for initializing populations, sine and cosine techniques are performed for updating prey positions which increases the ability of global exploration, and Cauchy mutation is used to acquire the optimal solutions. Initially, the CIC-IDS2017 dataset is used to evaluate the model performance and min-max normalization is utilized for normalizing the data. Then, IGJO is performed to select the appropriate features and Long Short-Term Memory (LSTM) is employed to classify IDS. The IGJO achieves a high accuracy of 99.95% compared to existing techniques respectively. © 2024 ieee.

关键词： Internet of things

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A Comparison of the Impact of Current Priority Modes of Inverter-Based Resources on the Performance of Grid Protective Relays 18

A Comparison of the Impact of Current Priority Modes of Inve...

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18th international conference on Protection and automation in Power Systems, IPAPS 2024

作者： Jafarian, Peyman Aminifar, Farrokh Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350350388

The dynamic response of inverter-based resources (IBRs) during fault-ride-though condition is driven by their control system, and it can be greatly different from that of synchronous generators (SGs). To standardize the fault response of IBRs, the recently published ieee Std. 2800-2022 has defined two different current control modes during abnormal voltage conditions, i.e. reactive current priority mode and active current priority mode. This paper compares these control modes based on their impacts on the grid protective relays. To do so, the reference currents generator of IBR is designed so as to utilize the maximum current capability of the IBR while complying with the requirements of the mentioned standard. Followed by modeling an IBR with its complete closed-loop control system, simulation studies are carried out on a test power system in the PSCAD/EMTDC software. The obtained results indicate that the reactive current priority mode is superior for the correct operation of grid protective relays. Whereas, the active current priority mode may lead to malfunction of some conventional protective relays. © 2024 ieee.

关键词： Relay protection

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IoT Based Smart Waste Monitoring System Envisioning a Sustainable Smart City

IoT Based Smart Waste Monitoring System Envisioning a Sustai...

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2024 ieee international conference on Computing, Applications and Systems, COMPAS 2024

作者： Aurko, Najmus Saquib Masum, S.M. Hossain, Md Sajid American International University Bangladesh Computer Science and Engineering Dhaka1229 Bangladesh American International University Bangladesh Electrical and Electronic Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331529765

This paper introduces a smart approach to waste management through the integration of smart technologies. Our proposed system employs the ESP8266 Wi-Fi module to enable remote notification, ensuring efficient waste monitoring both indoors and outdoors. The core components include an Ultrasonic Sensor HC-SR04 for real-time monitoring of the waste bin's fill level, an IR sensor to facilitate automated lid opening upon user proximity, and a servo motor to control the lid mechanism. Upon detection of fullness of the waste bin, the system triggers a buzzer, providing an audible alert for the user and encouraging the user timely waste disposal. The innovation aimed for a more cost effective and economically beneficial system. The innovation extends beyond conventional waste monitoring because we are not only implementing it indoors but also outdoors to monitor waste disposal which will maintain a clean environment overall. The paper outlines the implementation of a client-server model, emphasizing its potential to foster a pollution-free society, good health, and a cleaner environment. © 2024 ieee.

关键词： Ultrasonic sensors

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Analysis of the Power Curve and Development of A Graphic Interface for the Operation of the Synchronous Generator in the Machu Hydroelectric Plant 5

Analysis of the Power Curve and Development of A Graphic Int...

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5th international conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Cardenas, Kenyi Mendoza Rodriguez, Gabriel Osiris Cairampoma Universidad Continental School of Electrical Engineering Faculty of Engineering Huancayo Peru

ISBN: (纸本)9798350355253

The objective of the research was to develop a computational interface that allows the automatic drawing of the power chart and is compatible with current automation process equipment such as the HMI and SCADA for the H.C. Machu. The research approach is quantitative, the method is deductive-descriptive, the type of research carried out is applied, a descriptive level of research and the design was non-experimental and longitudinal. Obtaining as a result from the mathematical review of the construction of the power curves of the synchronous generators for both smooth poles and salient poles, the operating restrictions in operability limits, conditions of maximum exciter current, maximum stator current, maximum primary motor power, minimum starting power, were absorbed into Cartesian and polar expressions, in such a way that this allowed the delivery of the algorithms to be processed computationally. Concluding that it was possible to develop a graphical interface in the MATLAB software, which traces the power curve of the synchronous generators, the location of the operating points and control of the operability limits through the current of the excitation field applicable to the Machu Hydroelectric Power Plant. © 2024 ieee.

关键词： MATLAB

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