Search
23 articles for “Privacy-Preserving Machine Learning”
-
A Privacy-Preserving and Performance-Optimized Machine Learning Framework for Personalized E-Commerce User Experience
Abstract: In contemporary e-commerce websites, merchant suggestions are the most outstanding way to boost customer satisfaction and conversion levels. Nevertheless, the growing dependence on user data provokes significant concerns over the protection of privacy and efficiency in computation. In this paper, we have suggested a privacy-preserving and performance-optimized machine learning framework that allows customized e-commerce experiences and protects the sensitive information of users. The framework incorporates the concepts of federated learning …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 2, 2026 · pp. 1–8 Read article
-
Privacy-by-Design in Modern Data Science: A Systematic Review of Differential Privacy and Federated Analytics
Abstract: Okay so here is a problem that has been bothering us for a while. Everyone talks about how data is the new oil, how data-driven decisions are transforming healthcare, finance, governance, all of it. And that is true. But there is this massive elephant in the room that a lot of people in the data science community sort of wave away: privacy. The more useful the data, the more personal …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
-
Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and graphics processing unit (GPU)-based architectures without centralizing sensitive data. This work proposes a parallel adaptive federated learning (AFL) framework that integrates differential privacy and secure aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 09–16 Read article
-
Federated Intelligence for Privacy-Preserving Health Data Analytics
Abstract: Healthcare artificial intelligence is limited by a structural conflict: useful models benefit from diverse multi-institutional data, while clinical records cannot be freely pooled because of privacy, ownership and governance constraints. This study presents a practical federated-learning framework for privacy-preserving health data analytics and validates its basic learning behavior in a reproducible cross-silo simulation. Five non-identical institutional clients were created from the public Breast Cancer Wisconsin (Diagnostic) dataset, and a standardized …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 3, 2026 Read article
-
Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography
Abstract: In modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns …
Published in International Journal of Advanced Control and System Engineering · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
-
A Review of Machine Learning Applications in Web Data Mining
Abstract: The rapid development of Internet technology has resulted in a rapidly changing and intricate digital environment that requires new methods for organizing and evaluating online data. This study examines the use of machine learning (ML) in web data mining, focusing on its ability to extract relevant insights from huge amounts of online data. Web data mining, which is divided into three categories: content mining, structure mining, and use mining, uses …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 39–47 Read article
-
Privacy Preservation Methods in Multimedia Applications: A Comprehensive Review
Abstract: This comprehensive review explores the current landscape of privacy preservation techniques in multimedia applications, offering a detailed examination of their effectiveness, limitations, and future directions. As the use of multimedia data continues to grow across diverse sectors such as healthcare, surveillance, social media, and entertainment, ensuring the confidentiality and integrity of this data has become a pressing concern. The study covers a broad spectrum of privacy-preserving approaches, from conventional cryptographic …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 2, 2025 · pp. 33–41 Read article
-
Differential Privacy-Aware Data Sanitization for Multi-Level Security
Abstract: Multi-level security (MLS) models are fundamental for enforcing mandatory access control in high-security environments such as government, military, healthcare, and finance. However, traditional MLS frameworks, including the Bell-LaPadula and Biba models, often create rigid data silos, preventing efficient data utilization. Differential privacy (DP) presents a novel solution by enabling controlled information leakage while preserving confidentiality. By injecting statistical noise into query results, DP allows lower-clearance users to access sanitized versions …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 1, 2025 · pp. 42–52 Read article
-
Network Intrusion Detection System using Machine Learning and Deep Learning Approach
Abstract: Networks play a significant part in today’s world; fast internet and communication industries result in vast network size and data expansion. Furthermore, attackers aiming to launch various cyberattacks inside the system cannot be neglected. An IDS keeps track of the network’s software and hardware security to preserve its privacy, integrity, and accessibility. Despite the significant efforts of the researchers, current IDS continue to confront challenges in terms of accuracy rate, …
Published in Journal Of Network security · Vol. 10, Issue 1, 2022 · pp. 7–34 Read article
-
Multi-Layered AI-Driven Security in Wireless Ecosystems
Abstract: The proliferation of next-generation wireless technologies, from 5G/6G networks to the pervasive Internet of Things (IoT), has birthed a hyperconnected digital ecosystem of unprecedented scale and dynamism. This interconnectedness, however, introduces a vast and volatile attack surface, rendering conventional, signature-based security paradigms fundamentally obsolete. This paper posits that the only viable defense is an offensive, self-adaptive one, predicated on the integration of artificial intelligence (AI) directly into the wireless security …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 21–28 Read article
-
An Analysis of Machine Learning Models for Early Cardiac Risk Stratification
Abstract: The paper shows an in-depth study of machine learning and artificial intelligence solutions to early cardiac risk stratification which has a crucial necessity because cardiovascular disease (CVD) prediction remains a significant issue that needs to be improved beyond the conventional risk score. Since CVD is the most serious disease killer in the world, claiming 17.9 million deaths every year, there is a strong need to get the most sophisticated predictive …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
-
Evaluating Open-Source Large Language Models for Educational Applications
Abstract: Mobile healthcare technologies generate sensitive information through electronic health records, smartphones, wearable sensors, medical imaging systems, and remote monitoring platforms. These distributed data sources can improve disease prediction, continuous monitoring, clinical decision support, and personalized healthcare, but centralized collection creates substantial privacy, security, interoperability, and data-governance concerns. Federated learning (FL) provides a distributed machine learning paradigm in which participating healthcare institutions and personal devices collaboratively train models while retaining raw …
Published in Journal of Open Source Developments · Vol. 13, Issue 2, 2026 · pp. 10–21 Read article
-
Federated Learning: A Comprehensive Review of Models, Algorithms, and Business Applications
Abstract: In an age where data privacy is a significant concern, federated learning (FL) has become a game-changing method in machine learning. This decentralized model enables various parties to work together on training models without exchanging their raw data, effectively tackling the issues posed by data silos and privacy regulations. This article explores the current state of FL, including its underlying models and algorithms, practical applications, benefits, challenges, and future directions. …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 1–9 Read article
-
Machine Learning-Based Approach for Heart Disease Prediction
Abstract: Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 64–73 Read article
-
Overview AI-Driven Antenna Technologies and Privacy- Preserving Methods for Next-Generation 6G Wireless Systems
Abstract: The next generation of wireless communications, 6G, will be built on the convergence of artificial intelligence (AI) and advanced antenna systems. AI-driven antennas are poised to address the unprecedented requirements for data rate, reliability, adaptability, and ubiquity in future networks. An overview of current advancements in AI-enabled antenna systems for 6G networks is provided in this study. From traditional base station deployments to distributed, cell-free, and user-centric frameworks, it examines …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 1, 2026 · pp. 28–34 Read article
-
An Adaptive and Privacy-Aware Federated Learning Framework for Efficient and Secure Model Training Across Heterogeneous Datasets
Abstract: The problem of efficiency and privacy regarding heterogeneous data in modern distributed machine learning systems is a vital point that should be taken into account. The absence of IID data distribution, client heterogeneity, and privacy invasion during the aggregation model are the bane of conventional federated learning (FL) approaches to learning like FedAvg and FedProx. The paper proposes that the adaptive and privacy-aware FL framework (AFL-P) can be used to …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 16–25 Read article
-
A Study on Machine Learning and Cryptographic based Intrusion Detection System in Blockchain and Data Science Environment
Abstract: The recent speedy development of Internet of Things, vehicular networks and cloud computing has greatly boosted the need of effective and safe intrusion detection systems. The shortcomings of the traditional IDS-based approaches are centralized architecture, issue of scalability, and inability to analyze the encrypted and distributed data. To address these issues, the recent studies have paid attention to the incorporation of the latest technologies such as machine learning, deep learning, …
Published in International Journal of Wireless Network Security · Vol. 4, Issue 2, 2026 Read article
-
Cyberattack Detection System in Private Cloud
Abstract: As cloud computing adoption in colleges continues to rise, the security of private cloud systems has become a paramount concern. Data breaches resulting from cyberattacks can inflict severe damage to a university's revenue and reputation. This research proposes a novel machine learning-based cyber threat detection system tailored to the university's private cloud environment. The system's main objective is to continuously monitor the cloud infrastructure and employ advanced machine learning algorithms …
Published in Recent Trends in Parallel Computing · Vol. 10, Issue 2, 2023 · pp. 37–44 Read article
-
An Intelligent AI–ML Framework for Deepfake Generation and Detection
Abstract: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have led to the rise of deepfake technology, making it one of the most impactful innovations in the digital world. Deepfakes are synthetic forms of digital media generated using deep learning algorithms capable of realistically manipulating facial expressions, voice patterns, images, and video content. Although these technologies provide innovative applications in entertainment, virtual reality, healthcare, and education, their misuse has …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 2, 2025 · pp. 25–32 Read article
-
ENERGY CONSUMPTION MONITORING WITH A MOBILE APPLICATION
Abstract: Energy consumption monitoring via mobile applications has emerged as a crucial paradigm in modern smart grids and Internet of Things (IoT) frameworks, enabling real-time visibility and proactive energy management for end consumers. By bridging hardware sensors, wireless communication protocols, cloud analytics, and interactive mobile interfaces, these systems transform raw electrical telemetry into actionable insights. This technology addresses critical challenges such as peak demand management, energy inefficiency, phantom power loss, and …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 3, 2026 Read article