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,com...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 foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.展开更多
Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often proh...Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.展开更多
In recent years,the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being conne...In recent years,the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected.Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset,such as the NSL-KDD dataset.However,such approaches do not reect the features that exist in real medical scenarios,leading to failure in potential threat detection.To address this problem,we proposed a novel intrusion classication architecture known as a Multi-class Classication based Intrusion Detection Model(M-IDM),which typically relies on data collected by real devices and the use of convolutional neural networks(i.e.,it exhibits better performance compared with conventional machine learning algorithms,such as naïve Bayes,support vector machine(SVM)).Unlike existing studies,the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices,such as a patient’s monitors(i.e.,electrocardiogram and thermometers).The proposed architecture classies the data into multiple classes:Critical,informal,major,and minor,for intrusion detection.Further,we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms,including naïve Bayes,SVM,and logistic regression,using neural networks.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2022R1C1C2012463).
文摘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 foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)Program(IITP-2020-0-01816)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)This research was also supported by National Research Foundation(NRF)of Korea Grant funded by the Korean Government(MSIT)(No.2021R1A4A3022102).
文摘Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.
基金supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(Grant No.HI19C0839)。
文摘In recent years,the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected.Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset,such as the NSL-KDD dataset.However,such approaches do not reect the features that exist in real medical scenarios,leading to failure in potential threat detection.To address this problem,we proposed a novel intrusion classication architecture known as a Multi-class Classication based Intrusion Detection Model(M-IDM),which typically relies on data collected by real devices and the use of convolutional neural networks(i.e.,it exhibits better performance compared with conventional machine learning algorithms,such as naïve Bayes,support vector machine(SVM)).Unlike existing studies,the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices,such as a patient’s monitors(i.e.,electrocardiogram and thermometers).The proposed architecture classies the data into multiple classes:Critical,informal,major,and minor,for intrusion detection.Further,we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms,including naïve Bayes,SVM,and logistic regression,using neural networks.
