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Survey on the Applications of Differential Privacy 6

Survey on the Applications of Differential Privacy

引用

6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Cui, Jianhao Shen, Hua Cao, Ying School of Computer Science Hubei University Of Technology Wuhan China

ISBN: (纸本)9798331541750

In the era of big data and information explosion, the issue of personal information privacy protection has garnered increasing attention, leading to the emergence of various privacy protection schemes. Among these, differential privacy technology stands out due to its rigorous mathematical foundation and the ability to quantify privacy protection levels conveniently. Since its introduction by Dwork et al. in 2006, differential privacy has become a pivotal approach for addressing privacy concerns across a wide range of big data applications. To facilitate the flexible application of differential privacy in emerging domains, this paper provides a comprehensive overview of the existing research on differential privacy in the fields of data mining, and machine learning. We summarize the application frameworks, as well as the advantages and limitations, of differential privacy in these contexts. Finally, potential future directions for advancements in differential privacy technology are discussed. © 2024 IEEE.

关键词： data mining Differential privacy machine learning privacy protection

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Differentially Private Asynchronous Federated learning with Buffered Aggregation for Face recognition 6

Differentially Private Asynchronous Federated Learning with ...

引用

6th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Xing, Tongkun Zhang, Ming TravelSky Technology Limited Beijing China

ISBN: (纸本)9798350389418

the rise of privacy concerns in face recognition applications necessitates the development of secure machine learning frameworks. this paper introduces Differentially Private Asynchronous Federated learning with Buffered Aggregation (DP-AFLBA), a novel approach that combines differential privacy, asynchronous federated learning, and buffered aggregation to protect sensitive data while improving model accuracy. In DP-AFLBA, local model updates from participants are perturbed with carefully calibrated noise to ensure privacy, adhering to (ϵ, δ)-differential privacy standards. Asynchronous communication allows participants to send updates independently, reducing delays associated with traditional synchronous methods. To address the challenges of stale updates, a buffering mechanism aggregates updates over a defined time window, enabling the server to apply a more stable and accurate global model update. Experimental results demonstrate that DP-AFLBA achieves superior performance in terms of model accuracy and privacy protection compared to existing federated learning frameworks, making it particularly suitable for large-scale face recognition tasks. © 2024 IEEE.

关键词： Federated learning

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Predicting Crop Yield Using 3D Convolutional Neural Network with Dimension Reduction and Metric learning 6

Predicting Crop Yield Using 3D Convolutional Neural Network ...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wang, Ning Ma, Zhong Huo, Pengcheng Liu, Xi Xi'An Microelectronics Technology Institute Xi'an China

ISBN: (纸本)9798350325485

Crop yield prediction using remote sensing data during the growing season is helpful to farm planning and management, which has received more and more attention. Information mining from multi-channel geo-spatiotemporal data brings many benefits to crop yield prediction. However, most of the existing methods have not fully utilized the dimension reduction technology and the spatiotemporal feature of the data. In this paper, a new approach is proposed to predict the yield from multi-spatial images by using the dimensionality reduction method and the 3D convolutional neural network. In addition, counties with similar crop yields should have similar features learned by the network. thus, metric learning and multitask learning are used to learn more discriminating features. We evaluate the proposed method on county-level soybean yield prediction in the United States and the experimental results show the effectiveness of the proposed method. © 2023 IEEE.

关键词： Convolutional neural networks

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Application of data mining Technology in Urban Economic Statistical data Analysis 6

Application of Data Mining Technology in Urban Economic Stat...

引用

6th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2024

作者： Liu, Suyao Dalian University of Finance and Economics Liaoning Province Dalian116000 China

ISBN: (纸本)9798331541798

Under the background of economic globalization and informatization, the complexity and uncertainty of urban economic development situation are increasing day by day. Urban economic statistical data has also become an important carrier to reflect the operation of urban economy, and its accuracy and timeliness are directly related to the scientific and effective economic decision-making. However, the traditional data analysis and processing methods often follow the old logical framework, which shows obvious limitations in the face of massive, multidimensional and multi-source urban economic statistical data, and it is difficult to fully display the value behind urban economic statistics. In this regard, based on the actual application requirements, this paper will deeply analyze the application feasibility of data mining technology in urban economic statistical data analysis, and verify the application effect of data mining technology through actual case tests. Practice has proved that by K-means cluster analysis, urban economy can be divided into different regions or types, which reveals the economic characteristics and advantages of each region, and can significantly improve the efficiency and quality of urban economic statistical data analysis, thus providing more powerful technical support for urban economic development. © 2024 IEEE.

关键词： data accuracy

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pattern recognition for Uterine Fibroid Detection 6

Pattern Recognition for Uterine Fibroid Detection

引用

6th IEEE Eurasia conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Leong, Wai Yie Leong, Yuan Zhi Leong, Wai San Inti International University Persiaran Perdana Bbn Putra Nilai Nilai Malaysia Schneider Electric Singapore Pte. Ltd. Singapore

ISBN: (纸本)9798350396133

pattern recognition plays a significant role in the detection of uterine fibroids through various medical imaging modalities such as ultrasound, Magnetic Resonance Imaging (MRI), and computed tomography (CT) scans. Ultrasound imaging helps identify hypoechoic masses within the uterine muscle layer, while MRI provides detailed images showing well-defined masses with characteristic signal intensities on Tl-weighted and T2-weighted sequences. CT imaging, though less common, is used to detect fibroids based on hypodense masses within the uterus or pelvic cavity. Computer-aided diagnosis (CAD) systems and advanced machine-learning algorithms aid in automating the detection and characterization of fibroids by analyzing image features such as shape, texture, and intensity. these pattern recognition techniques facilitate early diagnosis and accurate assessment of uterine fibroids, enabling timely management and treatment of this prevalent gynecological condition. © 2024 IEEE.

关键词： Computerized tomography

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Virtual learning Environment to Predict Students' Academic Performance Using Deep learning Technique 6

Virtual Learning Environment to Predict Students' Academic P...

引用

6th international conference on Intelligent Computing in data Sciences, ICDS 2024

作者： Al Husaini, Yousuf Nasser Ahmad Shukor, Nur Syufiza Arab Open University Faculty of Computer Studies Muscat Oman Universiti Selangor Faculty of Communication Visual Art and Computing Shah Alam Malaysia

ISBN: (纸本)9798350351200

data mining in education has gained importance in recent decades due to the abundance of educational data available. the purpose of this study is to investigate deep learning and machine learning methods for predicting students' academic performance. the study employed machine learning techniques, such as the decision tree, random forest, and logistic regression, and deep learning techniques, such as Deep learning Long Short-Term Memory (LSTM). And it found that the LSTM model was best for sequential classification of time series and used the Open University learning Analytics dataset (OULAD) to develop predictive models. the LSTM achieved the highest accuracy, with an impressive 94.4%. this performance outperformed the decision tree, random forest, and logistic regression, models. the exceptional accuracy of the LSTM suggests that it can be a valuable tool for identifying at-risk students early and targeting interventions to improve their academic performance. this study recommends using this model in universities and colleges to predict the students' performance, and the prediction results would help improve the policymakers and decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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Federated learning and Convolutional Neural Networks for Intrusion Detection Systems 6

Federated Learning and Convolutional Neural Networks for Int...

引用

6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Abid, Malika Benkaddour, Mohammed Kamel Benouis, Mohamed Computer Science Department Ouargla Algeria University of Augsburg Human-Centered Artificial Intelligence Augsburg Germany

ISBN: (纸本)9798350350265

Recently, there has been a growing focus on network traffic threats due to the proliferation of mobile devices and the expanding Internet of things (IoT). the centralised anomaly detection approach implies sharing the user's data with a cloud server, which mostly exposes the user to the data breach issue. Given the above issues, this paper proposes a federated learning intrusion detection system (FedIDS) to improve the performance of intrusion detection while protecting user privacy. Motivated by recent exploits into deep learning for intrusion detection, we propose a convolutional neural network (CNN) model to learn and train the record data collected from independent and non-independent identically distributed (Non-IID, IID)data. this work conducts experiments on the NSL-KDD data set for network intrusion detection. Experimental results show that FedIDS has achieved a competitive advantage over state-of-the-art centralised machine learning methods. © 2024 IEEE.

关键词： Intrusion detection

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Large Language Models to Enhance Malware Detection in Edge Computing 6

Large Language Models to Enhance Malware Detection in Edge C...

引用

6th IEEE international conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Rondanini, Christian Carminati, Barbara Ferrari, Elena Kundu, Ashish Jajoo, Akshay University of Insubria DiSTA Italy Cisco Research United States

ISBN: (纸本)9798350386745

the increasing sophistication of malware in recent years has underscored the need for more advanced detection approaches, particularly in edge computing environments. Traditional methods and machine learning models, have shown promise but face significant limitations in handling evolving malware types. To address these challenges, we explore the application of Large Language Models (LLMs) for malware detection in edge computing. LLMs offer enhanced pattern recognition, contextual understanding, and the ability to analyze complex data, making them well-suited for dynamic, resource-constrained edge environments. However, their use introduces challenges such as model size and computational demands. In this paper, we propose a vision architecture that leverages LLMs' strengths while addressing their limitations, providing a framework for continuous learning and adaptability. We discuss the architecture's potential impact on improving malware detection as well as future research directions in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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data-Driven Decisions: Exploring the Impact of data mining in Healthcare 6

Data-Driven Decisions: Exploring the Impact of Data Mining i...

引用

6th international conference on Computing and Big data, ICCBD 2023

作者： Wang, Jin Cornely, Pierre-Richard Valdosta State University Department of Applied Mathematics and Physics ValdostaGA31698 United States College of Science and Mathematics Valdosta State University ValdostaGA31698 United States

ISBN: (纸本)9798350317008

In the healthcare sector, data mining stands as a crucial tool for analyzing vast patient datasets to unearth significant revelations. this manuscript provides a comprehensive exploration of data mining's contributions to healthcare, spanning from pandemic research, treatment efficacy assessment, predictive modeling, insurance fraud detection, medical device optimization, to streamlined hospital management. It delves into various data mining techniques such as clustering, classification, statistical analysis, unsupervised pattern recognition, and web-based data scrutiny, emphasizing their relevance in healthcare contexts. Algorithms like the Naïve Bayes Classifier and K-means clustering are underscored for their prevalent use in health-related scenarios. the treatise wraps up by shedding light on the emerging trajectories for healthcare data mining and pinpointing exciting avenues for impending inquiries. © 2023 IEEE.

关键词： data Analytics in Healthcare data Classification data Clustering Forecasting Models Fraud Prevention in Healthcare Insurance Healthcare Administration Internet-Based data mining Medical Equipment Pandemic Research Patient Management Quantitative Analysis Treatment Efficacy Unsupervised learning

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SSCL: Semi-supervised Contrastive learning for Industrial Anomaly Detection 6th

SSCL: Semi-supervised Contrastive Learning for Industrial An...

引用

6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Cai, Wei Gao, Jiechao Univ Sci & Technol Beijing Beijing Peoples R China Univ Virginia Charlottesville VA 22904 USA

ISBN: (纸本)9789819984619;9789819984626

Anomaly detection is an important machine learning task that aims to identify data points that are inconsistent with normal data patterns. In real-world scenarios, it is common to have access to some labeled and unlabeled samples that are known to be either normal or anomalous. To make full use of both types of data, we propose a semi-supervised contrastive learning method that combines self-supervised contrastive learning and supervised contrastive learning, forming a new framework: SSCL. Our method can learn a data representation that can distinguish between normal and anomalous data patterns, based on limited labeled data and abundant unlabeled data. We evaluate our method on multiple benchmark datasets, including MNIST, CIFAR-10 and industrial anomaly detection MVtec, STC. the experimental results show that our method achieves superior performance on all datasets compared to existing state-of-the-art methods.

关键词： Semi-supervised classification Anomaly detection Contrastive learning data representation

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