Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machin...Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.展开更多
This paper examines how the adoption of cloud computing affects the relationship between the technical and environmental capabilities of small and medium-sized enterprises(SMEs)in the tourism industry in Henan Provinc...This paper examines how the adoption of cloud computing affects the relationship between the technical and environmental capabilities of small and medium-sized enterprises(SMEs)in the tourism industry in Henan Province,China,thereby promoting the stable and sustainable development of the tourism industry,combining the laws of tourism market development,vigorously constructing a smart tourism project,guiding tourism cloud service providers to strengthen the cooperation and contact with the market’s tourism enterprises,introducing and utilizing cloud computing technology,optimizing and improving the functions of various tourism services of the enterprises,and enhancing the processing and analysis of enterprise-related data to provide tourism information.Strengthen the processing and analysis of enterprise-related data to provide tourism information,and further study the adoption of cloud computing and its impact on small and medium-sized enterprises(SMEs)in terms of technology and business environment knowledge,so as to make the best enterprise management decisions and realize the overall enhancement of the enterprise’s tourism brand value.展开更多
The current education field is experiencing an innovation driven by big data and cloud technologies,and these advanced technologies play a central role in the construction of smart campuses.Big data technology has a w...The current education field is experiencing an innovation driven by big data and cloud technologies,and these advanced technologies play a central role in the construction of smart campuses.Big data technology has a wide range of applications in student learning behavior analysis,teaching resource management,campus safety monitoring,and decision support,which improves the quality of education and management efficiency.Cloud computing technology supports the integration,distribution,and optimal use of educational resources through cloud resource sharing,virtual classrooms,intelligent campus management systems,and Infrastructure-as-a-Service(IaaS)models,which reduce costs and increase flexibility.This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses,showing how these technologies can contribute to the development of smart campuses,and laying the foundation for the future innovation of education models.展开更多
Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise inter...Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.展开更多
To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure ...To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology.The results show that the entity model generates greater hydrodynamic force during steady motion,whereas the square net model experiences greater force and moment during unsteady motion.The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation.The square net model also offers better static and dynamic stability,with a 24.5%increase in dynamic stability,achieving the highest lift-to-drag ratio at attack angles of 6°∼8°.This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.展开更多
With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communic...With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.展开更多
As the size of transistors shrinks and power density increases,thermal simulation has become an indispensable part of the device design procedure.However,existing works for advanced technology transistors use simplifi...As the size of transistors shrinks and power density increases,thermal simulation has become an indispensable part of the device design procedure.However,existing works for advanced technology transistors use simplified empirical models to calculate effective thermal conductivity in the simulations.In this work,we present a dataset of size-dependent effective thermal conductivity with electron and phonon properties extracted from ab initio computations.Absolute in-plane and cross-plane thermal conductivity data of eight semiconducting materials(Si,Ge,GaN,AlN,4H-SiC,GaAs,InAs,BAs)and four metallic materials(Al,W,TiN,Ti)with the characteristic length ranging from 5 nm to 50 nm have been provided.Besides the absolute value,normalized effective thermal conductivity is also given,in case it needs to be used with updated bulk thermal conductivity in the future.展开更多
With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT t...With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.展开更多
Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the clou...Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.展开更多
Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the centra...Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.展开更多
The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big ...The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.展开更多
This paper introduces the background of cloud computing, takes analysis of key technology, analysis of cloud computing and traditional competitive intelligence information model for comparative study, for the security...This paper introduces the background of cloud computing, takes analysis of key technology, analysis of cloud computing and traditional competitive intelligence information model for comparative study, for the security of cloud computing, function and service of the following aspects to elaborate, to explore the influence of cloud computing on the information.展开更多
Humanity is currently undergoing the fourth industrial revolution,characterized by advancements in artificial intelligence,clean energy,quantum information technology,virtual reality,and biotechnology.This technologic...Humanity is currently undergoing the fourth industrial revolution,characterized by advancements in artificial intelligence,clean energy,quantum information technology,virtual reality,and biotechnology.This technological revolution is poised to have a profound impact on the world.Quantum information technology encompasses both quantum computing and the transmission of quantum information.This article aims to integrate quantum information technology with international security concerns,exploring its implications for international security and envisioning its groundbreaking significance.展开更多
This article looks at the developments of computing in Papua New Guinea (PNG) in the post-independence era. More specifically, this article examines the development of national policies on Information and Communicatio...This article looks at the developments of computing in Papua New Guinea (PNG) in the post-independence era. More specifically, this article examines the development of national policies on Information and Communications Technology (ICT), digital technologies in PNG, and the development of computing education in PNG since 1975. The research findings reveal that PNG has made solid progress in computing, ICT, national ICT policies, digital technologies, and computing education at universities in the post-independence era. The proposed approach in this article might facilitate the research and development of computing, ICT, digital technologies, and big data analytics in PNG, and beyond.展开更多
Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved c...Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.展开更多
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several ...In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.展开更多
With the rapid development of information and communication technology （ICT） and sensor technology, ubiquitous computing （or pervasive computing） has become widely used with much convenience to human life. For ins...With the rapid development of information and communication technology （ICT） and sensor technology, ubiquitous computing （or pervasive computing） has become widely used with much convenience to human life. For instance, people can use their electronic devices at hand （e.g., Google glasses or Apple watch） to access information they need. However, this ＇~ubiquitous＂ service poses challenges to human autonomy. Based on the analysis of the features of pervasive computing, this paper points out the ambiguity between the subject and object of ubiquitous computing and shows technological interventions can affect human autonomy at three levels： technology addiction, the degradation of human capacities, and the reversal of the end and the means caused by the fuzziness of ma^-machine interface. In other words, ubiquitous computing gives people unprecedented convenience, and it also deprives of their freedom. According to Kant＇s Theory of Freedom, this article reflects on the relationship between the autonomy of technology and that of humankind.展开更多
基金supported in part by the National Natural Science Foundation of China(62462053)the Science and Technology Foundation of Qinghai Province(2023-ZJ-731)+1 种基金the Open Project of the Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area(2023-KF-12)the Open Research Fund of Guangdong Key Laboratory of Blockchain Security,Guangzhou University。
文摘Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.
文摘This paper examines how the adoption of cloud computing affects the relationship between the technical and environmental capabilities of small and medium-sized enterprises(SMEs)in the tourism industry in Henan Province,China,thereby promoting the stable and sustainable development of the tourism industry,combining the laws of tourism market development,vigorously constructing a smart tourism project,guiding tourism cloud service providers to strengthen the cooperation and contact with the market’s tourism enterprises,introducing and utilizing cloud computing technology,optimizing and improving the functions of various tourism services of the enterprises,and enhancing the processing and analysis of enterprise-related data to provide tourism information.Strengthen the processing and analysis of enterprise-related data to provide tourism information,and further study the adoption of cloud computing and its impact on small and medium-sized enterprises(SMEs)in terms of technology and business environment knowledge,so as to make the best enterprise management decisions and realize the overall enhancement of the enterprise’s tourism brand value.
文摘The current education field is experiencing an innovation driven by big data and cloud technologies,and these advanced technologies play a central role in the construction of smart campuses.Big data technology has a wide range of applications in student learning behavior analysis,teaching resource management,campus safety monitoring,and decision support,which improves the quality of education and management efficiency.Cloud computing technology supports the integration,distribution,and optimal use of educational resources through cloud resource sharing,virtual classrooms,intelligent campus management systems,and Infrastructure-as-a-Service(IaaS)models,which reduce costs and increase flexibility.This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses,showing how these technologies can contribute to the development of smart campuses,and laying the foundation for the future innovation of education models.
基金by National Natural Science Foundation of China(No.62306083)the Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-Z22175)the Ministry of Industry and Information Technology。
文摘Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.
基金supported by the National Key R&D Program of China(Grant No.2022YFB4703401).
文摘To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology.The results show that the entity model generates greater hydrodynamic force during steady motion,whereas the square net model experiences greater force and moment during unsteady motion.The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation.The square net model also offers better static and dynamic stability,with a 24.5%increase in dynamic stability,achieving the highest lift-to-drag ratio at attack angles of 6°∼8°.This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.
文摘With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.
基金Project supported by the National Key R&D Project from Ministry of Science and Technology of China(Grant No.2022YFA1203100)the National Natural Science Foundation of China(Grant No.52122606)the funding from Shanghai Polytechnic University.
文摘As the size of transistors shrinks and power density increases,thermal simulation has become an indispensable part of the device design procedure.However,existing works for advanced technology transistors use simplified empirical models to calculate effective thermal conductivity in the simulations.In this work,we present a dataset of size-dependent effective thermal conductivity with electron and phonon properties extracted from ab initio computations.Absolute in-plane and cross-plane thermal conductivity data of eight semiconducting materials(Si,Ge,GaN,AlN,4H-SiC,GaAs,InAs,BAs)and four metallic materials(Al,W,TiN,Ti)with the characteristic length ranging from 5 nm to 50 nm have been provided.Besides the absolute value,normalized effective thermal conductivity is also given,in case it needs to be used with updated bulk thermal conductivity in the future.
文摘With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.
基金This work was supported by the National Natural Science Foundation of China(No.61702276)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under Grant 2016r055 and the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.The authors are grateful for the anonymous reviewers who made constructive comments and improvements.
文摘Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.
基金supported by the National Natural Science Foundation of China(No.62206238)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220562)the Natural Science Research Project of Universities in Jiangsu Province(No.22KJB520010).
文摘Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.
文摘The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.
文摘This paper introduces the background of cloud computing, takes analysis of key technology, analysis of cloud computing and traditional competitive intelligence information model for comparative study, for the security of cloud computing, function and service of the following aspects to elaborate, to explore the influence of cloud computing on the information.
文摘Humanity is currently undergoing the fourth industrial revolution,characterized by advancements in artificial intelligence,clean energy,quantum information technology,virtual reality,and biotechnology.This technological revolution is poised to have a profound impact on the world.Quantum information technology encompasses both quantum computing and the transmission of quantum information.This article aims to integrate quantum information technology with international security concerns,exploring its implications for international security and envisioning its groundbreaking significance.
文摘This article looks at the developments of computing in Papua New Guinea (PNG) in the post-independence era. More specifically, this article examines the development of national policies on Information and Communications Technology (ICT), digital technologies in PNG, and the development of computing education in PNG since 1975. The research findings reveal that PNG has made solid progress in computing, ICT, national ICT policies, digital technologies, and computing education at universities in the post-independence era. The proposed approach in this article might facilitate the research and development of computing, ICT, digital technologies, and big data analytics in PNG, and beyond.
文摘Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
文摘In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.
文摘With the rapid development of information and communication technology （ICT） and sensor technology, ubiquitous computing （or pervasive computing） has become widely used with much convenience to human life. For instance, people can use their electronic devices at hand （e.g., Google glasses or Apple watch） to access information they need. However, this ＇~ubiquitous＂ service poses challenges to human autonomy. Based on the analysis of the features of pervasive computing, this paper points out the ambiguity between the subject and object of ubiquitous computing and shows technological interventions can affect human autonomy at three levels： technology addiction, the degradation of human capacities, and the reversal of the end and the means caused by the fuzziness of ma^-machine interface. In other words, ubiquitous computing gives people unprecedented convenience, and it also deprives of their freedom. According to Kant＇s Theory of Freedom, this article reflects on the relationship between the autonomy of technology and that of humankind.
