LMSA:A Lightweight Multi-Key Secure Aggregation Framework for Privacy-Preserving Healthcare AIoT  

作　　者：Hyunwoo Park Jaedong Lee 

机构地区：[1]Healthcare AI Team,National Cancer Center,Goyang,10408,Republic of Korea [2]Cancer Big Data and AI Branch,National Cancer Center,Goyang,10408,Republic of Korea [3]Department of Cancer AI&DigitalHealth,Graduate School of Cancer Science and Policy,National Cancer Center,Goyang,10408,Republic of Korea

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期827-847,共21页工程与科学中的计算机建模(英文)

基　　金：supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2022R1C1C2012463).

摘　　要：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 multikey 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 andmodular arithmetic for securemodel 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 foun

关 键 词：Secure aggregation LIGHTWEIGHT federated learning 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]