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Privacy and Security Challenges in Large Language Models 15

Privacy and Security Challenges in Large Language Models

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15th ieee Annual computing and Communication Workshop and conference, CCWC 2025

作者： Rathod, Vishal Nabavirazavi, Seyedsina Zad, Samira Iyengar, Sundararaja Sitharama Florida International University United States

ISBN: (纸本)9798331507695

Large Language Models (LLMs) are at the forefront of artificial intelligence advancements, demonstrating exceptional capabilities in natural language understanding and generation across diverse domains such as healthcare, finance, and customer service. However, their deployment introduces substantial secu-rity and privacy risks, including prompt injection, data leakage, and unauthorized data disclosures. These vulnerabilities highlight the need for robust frameworks to safeguard sensitive data and prevent misuse. This paper provides a comprehensive analysis of the security and privacy challenges in LLMs, examines existing mitigation strategies such as intelligent LLM firewalls, differen-tial privacy, and OW ASP-based security principles, and discusses future directions for ethical and secure LLM deployment. By addressing these challenges in detail, we identify gaps in current practices and propose a roadmap for the secure and responsible deployment of LLMs in high-stakes applications. Our findings underscore the importance of tailored security frameworks and privacy-preserving techniques to ensure the ethical and reliable use of LLMs in sensitive environments. Additionally, this pa-per emphasizes the significance of a human-in-the-loop (HITL) approach to ensure accountability and accuracy, particularly in critical domains. The discussion extends to emerging technologies such as retrieval-augmented generation (RAG) and adaptive threat detection systems, which hold promise for enhancing the security and ethical deployment of LLMs. © 2025 ieee.

关键词： Differential privacy

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Predictive Analysis of Lung Disease using Random Forest and Convolutional Neural Networks Techniques 1

Predictive Analysis of Lung Disease using Random Forest and ...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Ninawe, Sujal Sonar, Sunil Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavhan G H Raisoni College of Engineering Department of Data Science Nagpur India Nagpur India

ISBN: (纸本)9798331529246

In the contemporary era, technology is rapidly advancing in various sectors such as IT, banking, e-Commerce, and healthcare. This paper focuses on the healthcare sector, specifically on early detection of lung diseases. Basically, we compared CNN and Random Forest techniques to evaluate their accuracy in detecting lung diseases. The results showed that CNN achieved significantly higher accuracy, outperforming Random Forest due to its advanced feature extraction and learning capabilities. The approach involves analysing gray scale X-ray images to predict lung conditions like lung cancer, tuberculosis, pneumonia, and COVID-19 using Convolutional Neural Networks (CNN's). Additionally, the Random Forest algorithm is applied to evaluate the accuracy of lung disease detection. The CNN model achieved an accuracy of 91% across pneumonia, tuberculosis, COVID-19, and normal datasets, and 96% accuracy for lung cancer detection. The Random Forest algorithm further validated the system's effectiveness through precision, recall, and F1-score metrics. This research highlights the potential of combining CNNs with Random Forest to enhance lung disease detection, supporting early diagnosis and treatment planning. © 2025 ieee.

关键词： Lung cancer

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-Network on 6G-enabled Mobile Edge Networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 ieee.

关键词： Mobile edge computing

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advanced Parkinson’s Disease Classification using Hybrid Model: SVM and Random Forest 3

Advanced Parkinson’s Disease Classification using Hybrid Mo...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, S.H. Rajeshkannan, M. Anish, M.J. Sandeep, Pasupuleti Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331527549

Parkinson’s disease (PD) is a progressive and chronic neurodegenerative condition that significantly impairs motor and cognitive functions, posing a considerable burden on global healthcare systems. Early and precise diagnosis is vital to improving patient outcomes, yet traditional diagnostic techniques often encounter limitations due to overlapping symptoms with other neurological disorders and the subtle nature of early PD indicators. This research introduces a machine learning-driven approach that employs a hybrid ensemble model integrating Support Vector Machine (SVM) and Random Forest classifiers to enhance diagnostic accuracy by analyzing vocal and acoustic features. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enabling the model to identify meaningful patterns within underrepresented data. Principal Component Analysis (PCA) is utilized to reduce dimensionality, preserving critical data variance while minimizing computational complexity and mitigating overfitting. Hyperparameter tuning further optimizes each classifier, bolstering the model’s accuracy and robustness. Comparative analyses demonstrate that this hybrid ensemble model effectively distinguishes PD patients from healthy individuals, showcasing its potential as a reliable predictive tool in clinical applications. This study highlights the promise of advanced machine learning methodologies in supporting early PD detection, offering clinicians a valuable resource for timely intervention and improved patient care. © 2025 ieee.

关键词： hybrid classification Machine learning models Parkinson’s Disease principal component analysis (PCA) random forest algorithm support vector machine (SVM)

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Automating Talent acquisition Assisted by Resume Parsing System for Enhanced Job Matching 2

Automating Talent Acquisition Assisted by Resume Parsing Sys...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Gautam, Geetesh Sharma, Dolly Chaturvedi, Vijit Amity University Uttar Pradesh Noida India Amity Business School Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798331530389

Artificial intelligence has vast potential in resume parsing and human resource management. It aids in proliferating information and data sources, improving software's data management capabilities, and designing intricate and advanced algorithms. AI is changing the way firms hire and users interact with one another. This paper explores the development of a resume parsing system using the RoBERTa model to enhance job matching in talent acquisition. Traditional resume screening processes are time-consuming and prone to human error. Automated systems, although available, often require resumes to follow a specific format. This research presents a novel approach utilizing RoBERTa to parse resumes in various formats, thereby increasing the efficiency and accuracy of the talent acquisition process. The methodology includes the design and implementation of the system, followed by an evaluation of its performance. Results indicate significant improvements in processing time and matching accuracy. The implications of these findings suggest that organizations, regardless of size, can benefit from integrating such a system into their HR processes. © 2025 ieee.

关键词： Information management

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Machine Learning for the Analysis of Equipment Sensor data in Road Construction Projects 5th

Machine Learning for the Analysis of Equipment Sensor Data i...

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5th international conference on Transportation Geotechnics

作者： Silva, Raquel Fernandes, Hugo Neves, Jose Parente, Manuel Univ Minho Dept Civil Engn ISISE ARISE Guimaraes Portugal Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal Univ Lisbon Inst Super Tecn CERIS P-1049001 Lisbon Portugal

ISBN: (纸本)9789819782154;9789819782130;9789819782123

New trends in digitalization in construction have created opportunities for research and informed decision-making. Concepts like digital twins and sensorization have successfully enabled the direct collection of data from construction processes and equipment. For instance, integrating sensors into trucks transporting construction materials facilitates gathering valuable information about the equipment and the surrounding environment. This previously unattainable data can now be utilized to provide pertinent insights into the decision-making process. On one hand, accurate fuel consumption estimations are required to help optimization in construction and transportation infrastructure projects as they represent a major expense. On the other hand, despite the numerous studies conducted to detect cracks and potholes in road pavements, the classification of road types is frequently overlooked. This study aims to bridge this gap by developing a methodological framework that utilizes vibration data from sensors installed in construction trucks to predict the fuel consumption of heavy vehicles and the road category based on the pavement surface quality through which it is circulated. Given their promising results in prior research, the models Random Forest, Neural Network, and Support Vector Machine were applied to the database. The results demonstrate that vibration-based data acquisition methods combined with machine learning algorithms can accurately predict fuel consumption, identify different road categories, and can be successfully applied on a larger scale.

关键词： Fuel consumption Machine learning Road classification Sensors Supervised learning

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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

The integration of the Internet of Things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated Learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. This paves the way for a wide variety of intelligent IoT applications to be developed. This article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. These include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 ieee.

关键词： Federated learning

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 ieee.

关键词： ANN dropouts HIV/AIDS optimizers

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Proceedings of the 5th ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS'2009

Proceedings of the 5th IEEE International Workshop on Intell...

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5th ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS'2009

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Leveraging IoT and Machine Learning for intelligent Water Quality Monitoring systems

Leveraging IoT and Machine Learning for Intelligent Water Qu...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Baranwal, Gaurav Singh, Kaushtubh Gupta, Ayush Tiwari, Harsh N. Gupta, Tukur Ajay Kumar Garg Enginnering College Department of Electronics and communication Engineering Ghaziabad India

ISBN: (纸本)9798331527495

Water quality monitoring is a crucial aspect of public health, particularly in mitigating the risks associated with waterborne diseases and ensuring safe drinking water. Traditional monitoring methods are often labor-intensive, costly, and lack real-time capabilities, making them unsuitable for widespread and rural deployment. This paper examines the integration of advanced technologies, specifically the Internet of Things (IoT) and Machine Learning (ML), to design a sophisticated, low-cost water quality monitoring system. Leveraging an array of sensors, including pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), combined with the advanced embedded processor, the system ensures seamless data acquisition and processing. IoT enables continuous remote monitoring, while ML algorithms provide predictive analytics and intelligent anomaly detection to identify potential contaminants and trends. These value-driven solutions underscore the transformative potential of advanced technologies in revolutionizing water quality assessment. This review article carefully reviews the available literature on water quality assessment in order to lay out an assortment of conclusions on the concerns, problems, challenges, and research gaps that have emerged during the last few years. Research gaps are identified by thoroughly examining the previously suggested methodologies. The study presented in this article primarily identified the problems with the accuracy of the models, the generation of data collection mechanisms, and the types of data that were used in the suggested frameworks. Subsequently, research avenues towards smart water quality monitoring are offered by examining key concepts crucial to the expansion of this field of research. © 2025 ieee.

关键词： Damage detection

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