Cloud storage has gained increasing popularity,as it helps cloud users arbitrarily store and access the related outsourced data.Numerous public audit buildings have been presented to ensure data transparency.However,m...Cloud storage has gained increasing popularity,as it helps cloud users arbitrarily store and access the related outsourced data.Numerous public audit buildings have been presented to ensure data transparency.However,modern developments have mostly been constructed on the public key infrastructure.To achieve data integrity,the auditor must first authenticate the legality of the public key certificate,which adds to an immense workload for the auditor,in order to ensure that data integrity is accomplished.The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server.One of the main problems for individuals,though,is how to detect data integrity on a term where people have a backup of local files.Meanwhile,a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved.In this work,a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols.The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity.The idea has already been proved safe;its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.展开更多
A kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among Grid nodes, and the trustworthiness of nodes is eval...A kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among Grid nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayes method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling （DLS） algorithm, the Trust-Dynamic Level Scheduling （Trust-DLS） algorithm is proposed. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of Grid tasks in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in Grid environment.展开更多
We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set(RFS)theory.Specifically,we propose an adjusted version of the joint probabi...We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set(RFS)theory.Specifically,we propose an adjusted version of the joint probabilistic data association(JPDA)filter,known as the nearest-neighbor set JPDA(NNSJPDA).The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback-Leibler divergence,with the goal of improving the accuracy of the marginalization.Next,the distribution of the target-label vector is considered.The transition matrix of the target-label vector can be obtained after the switching of the posterior density.This transition matrix varies with time,causing the propagation of the distribution of the target-label vector to follow a non-homogeneous Markov chain.We show that the chain is inherently doubly stochastic and deduce corresponding theorems.Through examples and simulations,the effectiveness of NNSJPDA is verified.The results can be easily generalized to other data association approaches under the same RFS framework.展开更多
A novel scheme to joint phase noise （PHN） correcting and channel noise variance estimating for orthogonal frequency division multiplexing （OFDM） signal was proposed, The new scheme was based on the variational Bay...A novel scheme to joint phase noise （PHN） correcting and channel noise variance estimating for orthogonal frequency division multiplexing （OFDM） signal was proposed, The new scheme was based on the variational Bayes （VB） method and discrete cosine transform （DCT） approximation. Compared with the least squares （LS） based scheme, the proposed scheme could overcome the over-fitting phenomenon and thus lead to an improved performance. Computer simulations showed that the proposed VB based scheme outperforms the existing LS based scheme展开更多
文摘Cloud storage has gained increasing popularity,as it helps cloud users arbitrarily store and access the related outsourced data.Numerous public audit buildings have been presented to ensure data transparency.However,modern developments have mostly been constructed on the public key infrastructure.To achieve data integrity,the auditor must first authenticate the legality of the public key certificate,which adds to an immense workload for the auditor,in order to ensure that data integrity is accomplished.The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server.One of the main problems for individuals,though,is how to detect data integrity on a term where people have a backup of local files.Meanwhile,a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved.In this work,a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols.The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity.The idea has already been proved safe;its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.
基金the National Natural Science Foundation of China (Grant No. 60673157)the Ministry of Education Key Project (Grant No. 105071)SEC E-Institute: Shanghai High Institutions Grid (Grant No. 200301)
文摘A kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among Grid nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayes method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling （DLS） algorithm, the Trust-Dynamic Level Scheduling （Trust-DLS） algorithm is proposed. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of Grid tasks in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in Grid environment.
基金Project supported by the National Key Research and Development Program of China(No.2017YFB1402102)the National Natural Science Foundation of China(Nos.61907028 and 11872036)+2 种基金the Natural Science Foundation of Shaanxi Province,China(Nos.2020JQ-423,2019JQ-574,and 2019ZDLSF07-01)the Fundamental Research Funds for the Central Universities,China(No.GK201903103)the China Postdoctoral Science Foundation(No.2018M640950)。
文摘We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set(RFS)theory.Specifically,we propose an adjusted version of the joint probabilistic data association(JPDA)filter,known as the nearest-neighbor set JPDA(NNSJPDA).The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback-Leibler divergence,with the goal of improving the accuracy of the marginalization.Next,the distribution of the target-label vector is considered.The transition matrix of the target-label vector can be obtained after the switching of the posterior density.This transition matrix varies with time,causing the propagation of the distribution of the target-label vector to follow a non-homogeneous Markov chain.We show that the chain is inherently doubly stochastic and deduce corresponding theorems.Through examples and simulations,the effectiveness of NNSJPDA is verified.The results can be easily generalized to other data association approaches under the same RFS framework.
基金supported by the National Basic Research Program of China (2009CB320401)the National Key Scientific and Technological Project of China (2012ZX03004005-002,2010ZX03003-001)the National Natural Science Foundation of China (61171099)
文摘A novel scheme to joint phase noise （PHN） correcting and channel noise variance estimating for orthogonal frequency division multiplexing （OFDM） signal was proposed, The new scheme was based on the variational Bayes （VB） method and discrete cosine transform （DCT） approximation. Compared with the least squares （LS） based scheme, the proposed scheme could overcome the over-fitting phenomenon and thus lead to an improved performance. Computer simulations showed that the proposed VB based scheme outperforms the existing LS based scheme
