Suchergebnisse - Privacy-Preserving Techniques for Data Analysis and Machine Learning

An Optimized Multi-Task Learning Model for Disaster Classification and Victim Detection in Federated Learning Environments von Yi Jie Wong, Mau‐Luen Tham, Ban-Hoe Kwan, Ezra Morris, Yasunori Owada

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A Novel Global Prototype-Based Node Embedding Technique von Zyad Alkayem, Rami Zewail, Amin Shoukry, Daisuke Kawahara, Samir A. Elsagheer Mohamed

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Balancing Privacy and Utility in AI Training: Comparative Analysis of Privacy-Preserving Techniques von Maheriya, Arpita, Panchal, Shailesh

“… Conventional approaches frequently risk exposing sensitive information when sharing data. This research paper explores privacy-preserving machine learning techniques …”
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Privacy-Preserving Deep Learning on Machine Learning as a Service - A Comprehensive Survey von Tanuwidjaja, Harry Chandra, Choi, Rakyong, Baek, Seunggeun, Kim, Kwangjo

“… Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision-making, has become a hot commodity …”
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Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results von Li, Xiaoxiao, Gu, Yufeng, Dvornek, Nicha, Staib, Lawrence H., Ventola, Pamela, Duncan, James S.

“… •A novel framework for multi-site fMRI analysis without data-sharing using privacy-preserving federated learning …”
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NPMML: A Framework for Non-Interactive Privacy-Preserving Multi-Party Machine Learning von Li, Tong, Li, Jin, Chen, Xiaofeng, Liu, Zheli, Lou, Wenjing, Hou, Y. Thomas

“… In the recent decade, deep learning techniques have been widely adopted for founding artificial Intelligent applications, which led to successes in many data analysis tasks, such as risk assessment …”
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Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities von Shen, Meng, Tang, Xiangyun, Zhu, Liehuang, Du, Xiaojiang, Guizani, Mohsen

“… Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices …”
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Federated Learning With Privacy-Preserving Ensemble Attention Distillation von Gong, Xuan, Song, Liangchen, Vedula, Rishi, Sharma, Abhishek, Zheng, Meng, Planche, Benjamin, Innanje, Arun, Chen, Terrence, Yuan, Junsong, Doermann, David, Wu, Ziyan

“… Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized …”
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Privacy-preserving federated learning based on partial low-quality data von Wang, Huiyong, Wang, Qi, Ding, Yong, Tang, Shijie, Wang, Yujue

“… Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data …”
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Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey von Fouda, Mostafa M., Fadlullah, Zubair Md, Ibrahem, Mohamed I., Kato, Nei

“… lead to severe privacy concerns. Therefore, the concept of privacy-preserving data-driven learning models has recently emerged as a hot area of research to facilitate model training on large …”
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Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach von S, Selvakanmani, Dharani Devi, G, V, Rekha, Jeyalakshmi, J

“… Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification …”
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Multiparty Secure Broad Learning System for Privacy Preserving von Cao, Xiao-Kai, Wang, Chang-Dong, Lai, Jian-Huang, Huang, Qiong, Chen, C. L. Philip

“… Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML …”
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PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities von Kumar, Prabhat, Kumar, Randhir, Srivastava, Gautam, Gupta, Govind P., Tripathi, Rakesh, Gadekallu, Thippa Reddy, Xiong, Neal N.

“… ), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF …”
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High performance logistic regression for privacy-preserving genome analysis von De Cock, Martine, Dowsley, Rafael, Nascimento, Anderson C. A., Railsback, Davis, Shen, Jianwei, Todoki, Ariel

“… Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography …”
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Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment von Al-Qarafi, A., Alrowais, Fadwa, S. Alotaibi, Saud, Nemri, Nadhem, Al-Wesabi, Fahd N., Al Duhayyim, Mesfer, Marzouk, Radwa, Othman, Mahmoud, Al-Shabi, M.

“… In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving …”
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A Review on Privacy-Preserving Techniques for Spatiotemporal Data von AbuElHassan, Shadwa, Abo Alian, Alshaimaa, AbdelKader, Tamer, Badr, Nagwa

“… Although existing surveys have addressed privacy-preserving methods, many lack systematic methodological frameworks, rigorous quantitative comparisons, or in-depth analysis of emerging techniques …”
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Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud von Hu, Chenfei, Zhang, Chuan, Lei, Dian, Wu, Tong, Liu, Ximeng, Zhu, Liehuang

“… Several privacy-preserving machine learning schemes have been suggested recently to guarantee data and model privacy in the cloud …”
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Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices von Ferrag, Mohamed Amine, Ndhlovu, Mthandazo, Tihanyi, Norbert, Cordeiro, Lucas C., Debbah, Merouane, Lestable, Thierry, Thandi, Narinderjit Singh

“… IoT devices are expanding rapidly, resulting in a growing need for efficient techniques to autonomously identify network-based attacks in IoT networks with both high precision and minimal computational requirements …”
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Federated unsupervised random forest for privacy-preserving patient stratification von Pfeifer, Bastian, Sirocchi, Christel, Bloice, Marcus D, Kreuzthaler, Markus, Urschler, Martin

“… data’s role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing …”
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Secure Multiparty Computation for Privacy‐Preserving Machine Learning in Healthcare: A Comprehensive Survey von Naresh, Vankamamidi S., Venkata Raju, A., Srinivasa Rao, O.

“… ABSTRACT Privacy‐preserving machine learning (PPML) using secure multiparty computation (SMC …”
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