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Optimized Local Secret Sharing Techniques for distributed Blockchain networks 9

Optimized Local Secret Sharing Techniques for Distributed Bl...

引用

9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Roobini, M.S. Sindhuja, Chappidi Shravan, Chanda Nair, Manju C. Gowri, M. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai600119 India

ISBN: (纸本)9798350365092

In blockchain systems, transaction details are stored across a distributed ledger, and each peer bears the responsibility of upkeeping an identical replica of this ledger. This project explains the challenges faced by the high storage costs occurred in the traditional blockchain systems, where each peer maintains an identical copy of transaction data. A DSB system tries to tackle the high storage cost issue by incorporating cryptographic techniques. The distributed Storage Blockchain (DSB) systems, however face communication costs during peer failures, which are often caused by denial-of-service attacks. To overcome these challenges, the DSB approach has been using a LSS approach. This method utilizes a hierarchical privacy scheme comprising a primary global secret node and multiple subordinate local secret nodes. The suggested distributed Storage Blockchain (DSB) approach, incorporating Local Secret Sharing (LSS), enhances both storage efficiency and reduces recovery communication costs. This approach aims enhancing both the effectiveness of storage and reducing communication costs. During recovery processes. In the case of peer failures and security threats the DSB with LSS approach provides a solution for creating more resilient and resource-efficient blockchain systems. © 2024 IEEE.

关键词： Blockchain

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Prediction of Human Age and Gender Using Deep Learning For Smart security systems 2

Prediction of Human Age and Gender Using Deep Learning For S...

引用

2nd IEEE international conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kinthadi, Durga Bhavani Burugu, Abhishekar Rumandla, Anish Sujana, S. Vardhaman College of Engineering Dept. of Ece Telangana Hyderabad India

ISBN: (纸本)9798350347456

The demand for accurate identification and verification of a person has increased as the number of smart security systems has grown. In recent years, data from a human face has been used in numerous real-world applications, including social networking, security monitoring, advertising, and entertainment. computer vision researchers have long been interested in this topic because automatic age and gender prediction from facial images is crucial for interpersonal communication. This work predicts that the gender will be either 'Male' or 'Female,' and that the age will be one of the following ranges: (0-5), (6-10), (11-17), (18-25), (25-32), (33-45), (46-55), (55-70). In the proposed system the images are preprocessed and then the convolutional neural networks are used to extract age and gender-related features and classified the images using the appropriate classifiers. The face images are taken from the UTK dataset and the proposed method achieved a training accuracy of 92.5% and a validation accuracy of 90%. © 2023 IEEE.

关键词： security systems

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Comparative E-Voting security Evaluation: Multi-Modal Authentication Approaches 6

Comparative E-Voting Security Evaluation: Multi-Modal Authen...

引用

6th international conference on Blockchain Computing and Applications, BCCA 2024

作者： Ahmadieh, Elsi El Madhoun, Nour Lebanese University Faculty of Technology Department Communications and Computer Networks Engineering Beirut Lebanon LISITE Laboratory ISEP 10 Rue de Vanves Issy-les-Moulineaux92130 France Sorbonne Université CNRS LIP6 4 place Jussieu Paris75005 France

ISBN: (纸本)9798350351538

The decentralized, distributed ledger, a fundamental aspect of blockchain technology, plays a pivotal role in various sectors, including electronic voting (e-voting) systems. When considering e-voting, the significance of blockchain technology in boosting the security, transparency, and integrity of the entire process is evident. This ranges from the initial steps of user registration and authentication to the final stages of voting and result validation. Unlike conventional voting systems, which are plagued by issues such as fraud, tampering, and a lack of transparency in the result tallying and viewing processes, not to mention the delays, blockchain-enabled e-voting mitigates these challenges within government frameworks. It ensures a secure, tamper-proof, and transparent procedure, where each vote is recorded as an immutable transaction, thereby enhancing the process’s reliability. This paper explores the integration of voice recognition and fingerprinting using blockchain technology for enhancing the security of e-voting systems, comparing alternative authentication methods, and emphasizing the need for a multi-modal approach to achieve better security and reliability of the voting systems. © 2024 IEEE.

关键词： Voting machines

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The Internet of Trust: Securing and Transparent Supply Chains with Blockchain and IoT 18

The Internet of Trust: Securing and Transparent Supply Chain...

引用

18th IEEE international conference on Advanced networks and Telecommunications systems, ANTS 2024

作者： Singal, Gaurav Kushwaha, Riti Sharma, Ashish Tewari, Tulika Netaji Subhash University of Technology SMIEEE Computer Science and Engineering Delhi India Bennett University School of Computer Science and Technology Greater Noida India Manipal University Computer Science and Engineering Jaipur India Netaji Subhash University of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350391725

In an era where digital commerce continues to burgeon, the conventional supply chain confronts challenges of inefficiency, fraud, and a dearth of transparency. Blockchain, renowned for its decentralized and immutable ledger, emerges as a compelling solution to transform the e-commerce supply chain landscape. In conventional supply chain practices marked by centralized databases and manual record-keeping, inefficiencies and a dearth of transparency are widespread. Challenges stemming from cumbersome paperwork, delayed information flow, and vulnerability to errors hindered effective coordination, traceability, and overall operational efficiency. Blockchain and IoT can elevate e-commerce supply chains by ensuring transparent, secure, and automated data-sharing, fostering efficiency and trust throughout the entire process. We have presented an Ethereum-based blockchain and Internet of Things e-commerce platform in this paper. We describe in detail how to put in place a system that stores a product's journey's details on an immutable distributed ledger. This allows for secure tracing of a product's origin. The prototype of the system is deployed and tested on Hardhat testing framework. Traditional databases and cloud storages have been replaced by blockchain and IPFS. IPFS is used as the decentralized and distributed storage layer, facilitating the secure and efficient storage of credentials. Furthermore, the results of our experiments and security and scalability analyses demonstrate the viability of our suggested model. © 2024 IEEE.

关键词： distributed ledger

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Advancements in distributed Deep Learning: Federated Learning, AutoML Integration, and Beyond

Advancements in Distributed Deep Learning: Federated Learnin...

引用

2024 IEEE international conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Kavitha, T. Manikandan, S.P. Patil, Bhimaraya Patil, Anita Dept. of Computer Science and Engineering New Horizon College of Engineering Bangalore India CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg. Bangalore India Dept. of Information Science and Engg. City Engineering College Bengaluru India

ISBN: (纸本)9798331505134

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication overhead. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research. © 2024 IEEE.

关键词： Federated learning

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security Vulnerability Detection and Defense of Smart Home systems Based on the Internet of Things 6th

Security Vulnerability Detection and Defense of Smart Home S...

引用

6th international conference on Cyber security Intelligence and Analytics, CSIA 2024

作者： Zhao, Zhenghui Chen, Miao Chongqing Medical and Pharmaceutical College Chongqing401331 China

ISBN: (纸本)9783031882869

With the widespread application of IoT (Internet of Things) technology in smart homes, the security protection of systems has received more attention. In response to the problems of incomplete vulnerability detection and outdated defense mechanisms in traditional methods, this article proposed an efficient and comprehensive vulnerability detection and defense framework. By periodically scanning all terminal devices in the smart home system through edge devices, known vulnerabilities in the system were identified. Through edge computing technology and AI (Artificial Intelligence) algorithm detection, lightweight distributed defense mechanisms were deployed between intelligent devices to ensure that when an attack occurred, it can quickly respond locally and reduce the system response time. The experimental results showed that all devices in the edge computing-based scheme had a high coverage rate of more than 90%. In terms of the average response time, the edge computing defense framework combined with AI algorithm was 7.2 s, far lower than the traditional defense methods. The experimental results prove that this research is efficient in improving vulnerability detection and defense response. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Response time (computer systems)

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Concepts for Energy Management in the Evolution of Smart Grids 22nd

Concepts for Energy Management in the Evolution of Smart Gri...

引用

22nd international conference on Hybrid Intelligent systems, HIS 2022 and the 18th international conference on Information Assurance and security, IAS 2022

作者： Ritu, Ritu Department of Computer Science & Engineering APEX Institute of Technology Chandigarh University Punjab Mohali India

ISBN: (纸本)9783031274084

In the aim of a more sustainable and intelligent growth of distributed Electric systems, this paper presents an overview of fundamental power management ideas and technical difficulties for smart grid applications. Several possibilities are outlined, with an emphasis on the potential technical and economic benefits. The paper's third section looks at the fundamental issues of integrating electric cars into smart networks, which is one of the most important impediments to energy management in smart grid growth. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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Towards Smarter Maritime security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

引用

16th international conference on Ambient systems, networks and Technologies networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： distributed cloud

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Next Generation Firewall and Self Authentication for Network security 7

Next Generation Firewall and Self Authentication for Network...

引用

7th international conference on Image Information Processing, ICIIP 2023

作者： Singh, Bhagwant Cheema, Sikander Singh Punjabi University Department of Computer Science And Engineering Punjab Patiala India

ISBN: (纸本)9798350371406

In the era of the technologies, given the importance of the prevalence of the Internet, network safety is now essential to maintaining the accessibility and confidentiality of networks and data. Regular firewalls cannot deal with new dangers including targeted and data-focused attacks. The many types of current and next-generation firewalls are covered thoroughly in this study, emphasizing their possible capabilities. The Next Generation Firewall (NGFW)'s various network security mechanisms are discussed. Additionally, the benefits of modern firewalls versus conventional firewalls were contrasted. The introduction of smartphones and widespread technology that users connect to these systems, as well as the constantly shifting model connected to global networks like the Internet, are not supported by today's authorization systems. Several of the shortcomings of a conventional authentication method will be addressed by the idea of a self-authorizing framework for authenticating in an environment that is distributed. The article discusses the main objectives of network security, current security threats, and prospective network protection measures and self-authentication. © 2023 IEEE.

关键词： Network security

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Decentralized File Storage System 15

Decentralized File Storage System

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tiwari, Kshitij Dhanda, Namrata Verma, Rajat Amity University Computer Science & Engineering Department Uttar Pradesh Lucknow India Pranveer Singh Institute of Technology Computer Science & Engineering Department Uttar Pradesh Kanpur India

ISBN: (纸本)9798350370249

The need for safe, scalable, and effective cloud storage solutions has increased in a time when digital information is essential to society. Let us introduce you to Google Drive, a reliable cloud storage solution that is a must-have for both people and businesses. Nonetheless, concerns about data security, privacy, and single points of failure have been raised by Google Drive's centralization. With the goal of creating a more robust and resilient cloud storage environment, this project explores the decentralisation of Google Drive in an effort to allay these worries. The primary aim is to develop and execute a decentralised system by utilising innovative technologies like distributed file systems, peer-to-peer networks, and blockchain to transform the traditional paradigm of cloud storage. The trip begins with a detailed analysis of cloud storage systems as they exist now, highlighting the benefits and drawbacks of centralised designs. The theoretical underpinnings of decentralisation are then thoroughly examined, including important ideas, difficulties, and possible solutions. Important lessons are learned from current decentralised storage systems, offering a sophisticated perspective on their advantages and disadvantages. The development of a decentralised Google Drive prototype is key to the endeavour. In order to strengthen data integrity, security, and user privacy, this project seamlessly integrates blockchain technology. The implementation of a novel peer-to-peer network design distributes data among several nodes and eliminates the need for a central server. Furthermore, an advanced distributed file system is utilised to maximise the effectiveness of data retrieval and storage. © 2024 IEEE.

关键词： centralized decentralised google drive peer-topeer

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