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36 articles for “privacy-preserving”
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A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions
Abstract: The growing adoption of cloud computing for deploying artificial intelligence (AI) models has led to significant advancements in sectors such as healthcare, finance, and e-commerce. However, the integration of AI with cloud computing raises critical privacy concerns, particularly when handling sensitive data. This paper presents a comprehensive framework for implementing privacy-preserving AI models in cloud environments, addressing the unique challenges, and proposing effective solutions. The suggested framework employs advanced privacy-preserving …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 1–12 Read article
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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 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, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Privacy-preserving Multi-keyword Search in Multi-owner Setting Using Blockchain
Abstract: Searchable encryption (SE) has become an essential cryptographic technique, allowing users to securely search through encrypted data. However, most existing SE schemes rely on a single intermediary, such as a cloud server, leading to potential single-point failures, privacy breaches, and untrustworthy results. Many blockchain-based SE schemes have been proposed to address these issues. However, they frequently encounter difficulties such as supporting a multi-keyword, multi-owner model, ensuring query privacy, and maintaining …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 2, 2024 · pp. 12–18 Read article
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An Effective Privacy Preservation Technique for Enhancing Data Usability
Abstract: The rapid growth and adoption of modern database systems have created immense opportunities for researchers, industries, and organizations to extract meaningful knowledge and make data-driven decisions. While this progress has enabled the discovery of valuable patterns and trends, it has also intensified the challenge of safeguarding individual privacy. Merely removing direct identifiers such as names, social security numbers, or Aadhar card details is no longer sufficient, as adversaries can often …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 35–40 Read article
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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
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Advancements in Privacy-Preserving Techniques for Cloud Database Management Systems: A Review Analysis
Abstract: Cloud computing is a technology that provides a lot of configurable resources, enabling decentralized data execution and space. Cloud technologies have transformed the ideas of internal data storage and access to provide businesses with flexibility and efficiency. Thanks to cloud services like DBaaS, users may make use of advanced database features without having to worry about the burden of traditional databases. With more organizations moving to the usage of Cloud …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 11–22 Read article
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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
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An Overview of Privacy-Preserving Data Encryption Techniques in Mobile Cloud Computing for Big Data
Abstract: With the introduction of mobile cloud computing (MCC), data processing, storage, and sharing have undergone a radical transformation that has greatly improved organizational effectiveness and quality of life. But there are also serious worries about data security and privacy due to the increasing usage of mobile devices and cloud computing, particularly when managing large amounts of data from many sources like sensors and cellphones. The privacy issues surrounding MCC are …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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A Practical Approach for Privacy Preserving in Cloud Computing Using Fully Homomorphic Encryption Scheme BFV and CKKS
Abstract: Cloud computing is a new type of computing architecture whereby data may be accessed over the Internet along with other services linked to its scalable data centers in the cloud. The risk associated with computing is increased since it provides essential services that are typically provided to any third party, making it more difficult to enable data security, privacy, confidentiality, integrity, and authentication. To reduce security risks, most users choose …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 12, Issue 1, 2025 · pp. 1–8 Read article
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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
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Ethical and Responsible AI: A Comprehensive Review of Principles, Methods, and Tools
Abstract: Quick development of artificial intelligence (AI) has revolutionized a number of industries, including healthcare, banking, and government, by providing creative answers to challenging issues. However, there are serious ethical issues with growing integration of AI into crucial decision-making processes, including prejudice, a lack of transparency, abuses of data privacy, and accountability gaps. A systematic strategy that incorporates technical solutions, legal frameworks, and ethical standards is needed to address these issues. …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 23–34 Read article
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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
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Real-Time Browser-Based Early Warning System for Cyberbullying Detection in Online Platforms
Abstract: The rise in social networking through internet-based communication tools, Instagram, and YouTube, to name a few, significantly increases the risk of cyberbullying, thereby increasing psychological trauma on users, especially children, through adverse emotional states like anxiety, depression, etc. For a long time, researchers have been enhancing detection tools to counter cyberbullying, but their ability to detect only after the fact, along with limited support for English-based architecture, is a major …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 09–15 Read article
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Blockchain-Enabled Secure Data Sharing in Mobile IoT Networks
Abstract: The rapid proliferation of mobile Internet of Things (IoT) devices has resulted in an exponential increase in data generation, storage, and sharing, which poses significant challenges related to security, privacy, integrity, and trustworthiness. Traditional centralized architectures for IoT data exchange are inherently vulnerable to single points of failure, unauthorized access, data tampering, and limitations in scalability. Blockchain technology, with its decentralized ledger structure, cryptographic integrity, and consensus mechanisms, provides a …
Published in International Journal of Mobile Computing Technology · Vol. 4, Issue 1, 2026 · pp. 38–44 Read article
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Ensuring Data Traceability Across Multiple Cloud Environments
Abstract: This study investigates the challenges and solutions for ensuring data traceability across multiple cloud environments. With organizations' increasing reliance on cloud infrastructure, maintaining data traceability is crucial for compliance, data integrity, and secure data management. The diversity of cloud systems, spanning public, private, and hybrid models, introduces complexities in tracking data lineage, access, and movement. This study delves into multi-cloud strategies' technical and operational hurdles, such as varying data formats, …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 08–22 Read article
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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
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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
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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
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A Mathematical Perspective on Recent Cloud-Computing Trends for Scalable and Secure Social-Media Platforms
Abstract: Cloud computing underpins modern social-media platforms by providing elastic compute, storage, and data-processing pipelines capable of absorbing highly bursty workloads. This paper surveys recent cloud-native trends—serverless and event-driven design, container orchestration, edge/CDN offload, streaming analytics, and privacy-enhancing security controls—and formalizes their impact through a compact mathematical model. We express workload volatility using arrival-rate functions, use queueing-based capacity sizing to derive auto-scaling rules, and formulate an optimization objective that balances cost …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 35–40 Read article
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Digital Voting System Using Blockchain
Abstract: The advent of blockchain technology has ushered in a new era of secure, transparent, and decentralized systems, providing a promising foundation for various applications, including electronic voting (e-voting). This report examines the development and deployment of a blockchain-based e-voting system designed to tackle the ongoing issues of security, transparency, and voter privacy in elections. The system utilizes blockchain's core features, including immutability, decentralization, and cryptographic security, to establish a secure …
Published in Journal of Web Engineering & Technology · Vol. 11, Issue 3, 2024 · pp. 8–14 Read article