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
Data aggregation technology reduces traffic overhead of wireless sensor network and extends effective working time of the network,yet continued operation of wireless sensor networks increases the probability of aggreg...Data aggregation technology reduces traffic overhead of wireless sensor network and extends effective working time of the network,yet continued operation of wireless sensor networks increases the probability of aggregation nodes being captured and probability of aggregated data being tampered.Thus it will seriously affect the security performance of the network. For network security issues,a stateful public key based SDAM( secure data aggregation model) is proposed for wireless sensor networks( WSNs),which employs a new stateful public key encryption to provide efficient end-to-end security. Moreover,the security aggregation model will not impose any bound on the aggregation function property,so as to realize the low cost and high security level at the same time.展开更多
As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protoco...As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of data.The performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN.In consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this paper.Then,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data aggregation.Along this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for IWSN.Finally,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.展开更多
The development of data-driven artificial intelligence technology has given birth to a variety of big data applications.Data has become an essential factor to improve these applications.Federated learning,a privacy-pr...The development of data-driven artificial intelligence technology has given birth to a variety of big data applications.Data has become an essential factor to improve these applications.Federated learning,a privacy-preserving machine learning method,is proposed to leverage data from different data owners.It is typically used in conjunction with cryptographic methods,in which data owners train the global model by sharing encrypted model updates.However,data encryption makes it difficult to identify the quality of these model updates.Malicious data owners may launch attacks such as data poisoning and free-riding.To defend against such attacks,it is necessary to find an approach to audit encrypted model updates.In this paper,we propose a blockchain-based audit approach for encrypted gradients.It uses a behavior chain to record the encrypted gradients from data owners,and an audit chain to evaluate the gradients’quality.Specifically,we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation,ensuring the availability of aggregated gradient.In addition,we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients.Through security analysis and experimental evaluation,we demonstrate that our approach can defend against malicious gradient attacks in federated learning.展开更多
The Wireless Sensor Networks(WSNs)used for the monitoring applications like pipelines carrying oil,water,and gas;perimeter surveillance;border monitoring;and subway tunnel monitoring form linearWSNs.Here,the infrastru...The Wireless Sensor Networks(WSNs)used for the monitoring applications like pipelines carrying oil,water,and gas;perimeter surveillance;border monitoring;and subway tunnel monitoring form linearWSNs.Here,the infrastructure being monitored inherently forms linearity(straight line through the placement of sensor nodes).Therefore,suchWSNs are called linear WSNs.These applications are security critical because the data being communicated can be used for malicious purposes.The contemporary research of WSNs data security cannot fit in directly to linear WSN as only by capturing few nodes,the adversary can disrupt the entire service of linear WSN.Therefore,we propose a data aggregation scheme that takes care of privacy,confidentiality,and integrity of data.In addition,the scheme is resilient against node capture attack and collusion attacks.There are several schemes detecting the malicious nodes.However,the proposed scheme also provides an identification of malicious nodes with lesser key storage requirements.Moreover,we provide an analysis of communication cost regarding the number of messages being communicated.To the best of our knowledge,the proposed data aggregation scheme is the first lightweight scheme that achieves privacy and verification of data,resistance against node capture and collusion attacks,and malicious node identification in linear WSNs.展开更多
With the rapid development of mobile devices,aggregation security and efficiency topics are more important than past in crowd sensing.When collecting large-scale vehicle-provided data,the data transmitted via autonomo...With the rapid development of mobile devices,aggregation security and efficiency topics are more important than past in crowd sensing.When collecting large-scale vehicle-provided data,the data transmitted via autonomous networks are publicly accessible to all attackers,which increases the risk of vehicle exposure.So we need to ensure data aggregation security.In addition,low aggregation efficiency will lead to insufficient sensing data,making the data unable to provide data mining services.Aiming at the problem of aggregation security and efficiency in large-scale data collection,this article proposes a data collection mechanism(VDCM)for crowd sensing in vehicular ad hoc networks(VANETs).The mechanism includes two mechanism assumptions and selects appropriate methods to reduce consumption.It selects sub mechanism 1 when there exist very few vehicles or the coalition cannot be formed,otherwise selects sub mechanism 2.Single aggregation is used to collect data in sub mechanism 1.In sub mechanism 2,cooperative vehicles are selected by using coalition formation strategy and auction cooperation agreement,and multi aggregation is used to collect data.Two sub mechanisms use Paillier homomorphic encryption technology to ensure the security of data aggregation.In addition,mechanism supplements the data update and scoring steps to increase the amount of available data.The performance analysis shows that the mechanism proposed in this paper can safely aggregate data and reduce consumption.The simulation results indicate that the proposed mechanism reduces time consumption and increases the amount of available data compared with existing mechanisms.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Support by the National High Technology Research and Development Program of China(No.2012AA120802)the National Natural Science Foundation of China(No.61302074)+1 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20122301120004)Natural Science Foundation of Heilongjiang Province(No.QC2013C061)
文摘Data aggregation technology reduces traffic overhead of wireless sensor network and extends effective working time of the network,yet continued operation of wireless sensor networks increases the probability of aggregation nodes being captured and probability of aggregated data being tampered.Thus it will seriously affect the security performance of the network. For network security issues,a stateful public key based SDAM( secure data aggregation model) is proposed for wireless sensor networks( WSNs),which employs a new stateful public key encryption to provide efficient end-to-end security. Moreover,the security aggregation model will not impose any bound on the aggregation function property,so as to realize the low cost and high security level at the same time.
基金partially supported by the National Natural Science Foundation of China(61571004)the Shanghai Natural Science Foundation(No.17ZR1429100)+1 种基金the National Science and Technology Major Project of China(No.2018ZX03001017-004)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.YJKYYQ20170074).
文摘As an Industrial Wireless Sensor Network(IWSN)is usually deployed in a harsh or unattended environment,the privacy security of data aggregation is facing more and more challenges.Currently,the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating,alternately,the aim at enhancing the security of data.The performances of the secure data aggregation protocols are the trade-off of several metrics,which involves the transmission/fusion,the energy efficiency and the security in Wireless Sensor Network(WSN).Unfortunately,there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN.In consideration of IWSN,we firstly review the security requirements and techniques in WSN data aggregation in this paper.Then,we give a holistic overview of the classical secure data aggregation protocols,which are divided into three categories:hop-by-hop encrypted data aggregation,end-to-end encrypted data aggregation and unencrypted secure data aggregation.Along this way,combining with the characteristics of industrial applications,we analyze the pros and cons of the existing security schemes in each category qualitatively,and realize that the security and the energy efficiency are suitable for IWSN.Finally,we make the conclusion about the techniques and approach in these categories,and highlight the future research directions of privacy preserving data aggregation in IWSN.
基金This research is sponsored by the National Key R&D Program of China(No.2018YFB2100400)the National Natural Science Foundation of China(No.62002077,61872100)+3 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110385)Strategic Research and Consultation Project of the Chinese Academy of Engineering(No.2021-HYZD-8-3)the China Postdoctoral Science Foundation(No.2020M682657)Zhejiang Lab(No.2020NF0AB01).
文摘The development of data-driven artificial intelligence technology has given birth to a variety of big data applications.Data has become an essential factor to improve these applications.Federated learning,a privacy-preserving machine learning method,is proposed to leverage data from different data owners.It is typically used in conjunction with cryptographic methods,in which data owners train the global model by sharing encrypted model updates.However,data encryption makes it difficult to identify the quality of these model updates.Malicious data owners may launch attacks such as data poisoning and free-riding.To defend against such attacks,it is necessary to find an approach to audit encrypted model updates.In this paper,we propose a blockchain-based audit approach for encrypted gradients.It uses a behavior chain to record the encrypted gradients from data owners,and an audit chain to evaluate the gradients’quality.Specifically,we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation,ensuring the availability of aggregated gradient.In addition,we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients.Through security analysis and experimental evaluation,we demonstrate that our approach can defend against malicious gradient attacks in federated learning.
文摘The Wireless Sensor Networks(WSNs)used for the monitoring applications like pipelines carrying oil,water,and gas;perimeter surveillance;border monitoring;and subway tunnel monitoring form linearWSNs.Here,the infrastructure being monitored inherently forms linearity(straight line through the placement of sensor nodes).Therefore,suchWSNs are called linear WSNs.These applications are security critical because the data being communicated can be used for malicious purposes.The contemporary research of WSNs data security cannot fit in directly to linear WSN as only by capturing few nodes,the adversary can disrupt the entire service of linear WSN.Therefore,we propose a data aggregation scheme that takes care of privacy,confidentiality,and integrity of data.In addition,the scheme is resilient against node capture attack and collusion attacks.There are several schemes detecting the malicious nodes.However,the proposed scheme also provides an identification of malicious nodes with lesser key storage requirements.Moreover,we provide an analysis of communication cost regarding the number of messages being communicated.To the best of our knowledge,the proposed data aggregation scheme is the first lightweight scheme that achieves privacy and verification of data,resistance against node capture and collusion attacks,and malicious node identification in linear WSNs.
基金supported in part by the National Natural Science Foundation of China(Nos.62272195 and 61802146)the Guangdong Province Science and Technology Planning Project(No.KTP20200022)+3 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2019A1515011017)the Science and Technology Program of Guangzhou of China(No.202201010421)the Fundamental Research Funds for the Central Universities(Nos.21621417 and 21622402)the Guangdong Provincial Key Laboratory of Cyber and Information Security Vulnerability Research(No.2020B1212060081).
文摘With the rapid development of mobile devices,aggregation security and efficiency topics are more important than past in crowd sensing.When collecting large-scale vehicle-provided data,the data transmitted via autonomous networks are publicly accessible to all attackers,which increases the risk of vehicle exposure.So we need to ensure data aggregation security.In addition,low aggregation efficiency will lead to insufficient sensing data,making the data unable to provide data mining services.Aiming at the problem of aggregation security and efficiency in large-scale data collection,this article proposes a data collection mechanism(VDCM)for crowd sensing in vehicular ad hoc networks(VANETs).The mechanism includes two mechanism assumptions and selects appropriate methods to reduce consumption.It selects sub mechanism 1 when there exist very few vehicles or the coalition cannot be formed,otherwise selects sub mechanism 2.Single aggregation is used to collect data in sub mechanism 1.In sub mechanism 2,cooperative vehicles are selected by using coalition formation strategy and auction cooperation agreement,and multi aggregation is used to collect data.Two sub mechanisms use Paillier homomorphic encryption technology to ensure the security of data aggregation.In addition,mechanism supplements the data update and scoring steps to increase the amount of available data.The performance analysis shows that the mechanism proposed in this paper can safely aggregate data and reduce consumption.The simulation results indicate that the proposed mechanism reduces time consumption and increases the amount of available data compared with existing mechanisms.
