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5 results for “Privacy-Preserving Machine Learning”
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Research ArticleOpen accessIn the keywordsPrivacy-Preserving Deep Federated Learning on the Edge Using Homomorphic Encryption and Secure Multiparty ComputationNoman Aasif Gudur, Mohamed El-Dosuky, Sherif KamelThe increasing volume of consumer data necessitates reliable edge devices for personalized user experiences. Federated Learning (FL) offers a state-of-the-art approach to decentralized machine learning by leveraging data distributed across multiple client devices. However, user data privacy remains vulnerable to corruption through feature heterogeneity and malicious attacks. While several privacy-preserving techniques have been previously implemented, they suffer from implementation constraints … …Journal of Computer Science, Vol. 21, Issue 11 · 21 Dec 2025 · 4,381 accessesView PDFCite
Review ArticleOpen accessIn the abstractHybrid Machine Learning Framework with Data Analytics Model for Privacy-Preserved Intelligent Predictive Maintenance in Healthcare IoTArun Ganji, D. Usha, P.S. RajakumarFederated Learning (FL) is a cutting-edge approach for developing machine learning (ML) models using distributed datasets while preserving data privacy and ownership. FL is particularly suited for Internet of Things (IoT) networks due to its decentralized nature, which supports In-Edge AI and maintains data locality. However, FL's complexity poses challenges in analyzing system health, making it crucial to develop robust … …Journal of Computer Science, Vol. 21, Issue 1 · 30 Nov 2024 · 5,821 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordEnhancing the CIC IoT Dataset 2023 for Improved Attack Detection through GANs Augmentation and Federated LearningShahad Alahmari, Noura AleisaThe escalating frequency and sophistication of cyber-attacks on Internet of Things (IoT) devices present a pressing challenge to cybersecurity. With IoT device connections projected to exceed 42 billion by 2025, the vulnerability of these devices to cyber-attacks has never been more evident. This paper investigates the integration of Machine Learning (ML) and data augmentation, specifically Generative Adversarial Networks (GAN) and … …Journal of Computer Science, Vol. 21, Issue 7 · 18 Jul 2025 · 9,111 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordFed-Deep CTRL (Light GBM)-Scalable, Interpretable, and Privacy-Preserving Federated Learning Framework for Wireless Intrusion Detection SystemSudaroli Vijayakumar, V. MuthumanikandanA learning-based intrusion detection system automates the understanding and reporting of network traffic information. The systems using neural networks and machine learning have demonstrated good detection accuracy, but the accuracy is entirely reliant on the type and volume of data. Additionally, there are issues with its scalability, privacy, efficiency, and interpretability. With the innovative fed-deep CTRL (light GBM) method, this … …Journal of Computer Science, Vol. 20, Issue 4 · 29 Feb 2024 · 5,321 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordCustomized Privacy Preservation Using Unknowns to Stymie Unearthing Of Association RulesJ. Indumathi, G.V. UmaThe explosions of new data mining techniques has augmented privacy risks because now it is probable to powerfully coalesce and cross-examine massive data stores, accessible on the web, in the rummage around of earlier unidentified hidden patterns. Consecutively to make a overtly accessible system safe and sound, we must guarantee not only that private sensitive data have been trimmed out, … …Journal of Computer Science, Vol. 3, Issue 11 · 30 Nov 2007 · 7,117 accessesView PDFCite