Blockchain generation has the capability to revolutionize the management and sharing of Electronic Medical Records (EMRs). By using a distributed ledger, blockchain can set up a steady document of transactions, making...
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In today's fast paced world, managing daily tasks can be difficult. In order to avoid this challenge, the concept of reminder system is used in homes to remind the people about their day to day regular activities....
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A Vehicular Ad-hoc Network is an experimental approach for sending or receiving data or messages for secure driving. While sharing data, attacks can occur using malware. To avoid this there is a need for an algorithm ...
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This paper presents BC-SBOM, a novel blockchainbased system designed to enhance the management of Software Bills of Materials (SBOMs). By leveraging blockchain technology, BC-SBOM ensures secure storage and sharing of...
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ISBN:
(纸本)9791188428137
This paper presents BC-SBOM, a novel blockchainbased system designed to enhance the management of Software Bills of Materials (SBOMs). By leveraging blockchain technology, BC-SBOM ensures secure storage and sharing of SBOMs, while providing a comprehensive global view of dependencies among software components. The system also supports rapid propagation of alerts for newly discovered vulnerabilities, thereby increasing responsiveness to potential threats. Offering superior reliability, transparency, and availability compared to traditional SBOM tools, BC-SBOM aims to significantly improve the management of complex software systems and contribute to the advancement of software security practices. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific detai...
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The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and
Prior methodologies for the detection of scene text have demonstrated promising outcomes across many evaluation metrics. Nevertheless, despite the utilization of deep neural network models, their effectiveness is some...
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Glaucoma, a major contributor to irreversible blind-ness, which requires early diagnosis and appropriate treatment. In this work, we use deep learning techniques to automate the diagnosis of glaucoma from retinal fund...
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This research endeavor constitutes an intricate fusion of advanced quantitative methodologies within a cutting-edge web-based predictive analytics framework tailored for stock market forecasting. The amalgamation of f...
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With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilisti...
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3-d object pose estimation is an essential mission for expertise three-D scenes, and it has won sizeable attention in current years, its various applications in robotics, augmented reality, and autonomous riding. Deep...
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