LMSA: A Lightweight Multi-Key Secure Aggregation Framework for Privacy-Preserving Healthcare AIoT
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| Publicado en: | Computer Modeling in Engineering & Sciences vol. 143, no. 1 (2025), p. 827 |
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Tech Science Press
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 001 | 3200122643 | ||
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| 022 | |a 1526-1492 | ||
| 022 | |a 1526-1506 | ||
| 024 | 7 | |a 10.32604/cmes.2025.061178 |2 doi | |
| 035 | |a 3200122643 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Park, Hyunwoo | |
| 245 | 1 | |a LMSA: A Lightweight Multi-Key Secure Aggregation Framework for Privacy-Preserving Healthcare AIoT | |
| 260 | |b Tech Science Press |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing, achieving O(n) complexity with AES (Advanced Encryption Standard)-256-level security; (2) a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR (CounTeR) encryption and modular arithmetic for secure model weight combination; and (3) a resource-optimized implementation utilizing AES-NI (New Instructions) instructions and efficient memory management for real-time operations on constrained devices. Experimental evaluations using the National Institutes of Health (NIH) Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer (ViT), ResNet-50, and MobileNet architectures across distributed healthcare institutions. Memory usage analysis confirmed minimal overhead, with ViT (327.30 MB), ResNet-50 (89.87 MB), and MobileNet (8.63 MB) maintaining stable encryption times across communication rounds. LMSA ensures robust security through hardware acceleration, enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance. Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous, real-world environments. LMSA represents a foundational advancement for privacy-conscious healthcare AI applications, bridging the gap between privacy and performance. | |
| 653 | |a Encryption | ||
| 653 | |a Real time operation | ||
| 653 | |a Security | ||
| 653 | |a Health care | ||
| 653 | |a Hardware | ||
| 653 | |a Prediction models | ||
| 653 | |a Privacy | ||
| 653 | |a Cryptography | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Federated learning | ||
| 653 | |a Constraints | ||
| 653 | |a Distributed memory | ||
| 653 | |a Memory management | ||
| 700 | 1 | |a Lee, Jaedong | |
| 773 | 0 | |t Computer Modeling in Engineering & Sciences |g vol. 143, no. 1 (2025), p. 827 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3200122643/abstract/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3200122643/fulltextPDF/embedded/Y2VX53961LHR7RE6?source=fedsrch |