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3 articles for “differential privacy (DP)”
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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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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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Ethical AI and Data Protection in the Era of Industry 5.0
Abstract: This paper provides a comprehensive analysis of the critical intersection between Responsible AI (RAI), data privacy, and the Industry 5.0 paradigm. Industry 5.0, defined by its human-centric, sustainable, and resilient pillars, introduces a fundamental paradox: its core requirement for human-AI collaboration necessitates the collection and processing of granular human data, creating direct conflicts with emerging global data privacy and AI regulations. This research utilizes a systematic integrative review methodology, analyzing …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article