In order to achieve fine-grained access control in cloud computing,existing digital rights management(DRM) schemes adopt attribute-based encryption as the main encryption primitive.However,these schemes suffer from in...In order to achieve fine-grained access control in cloud computing,existing digital rights management(DRM) schemes adopt attribute-based encryption as the main encryption primitive.However,these schemes suffer from inefficiency and cannot support dynamic updating of usage rights stored in the cloud.In this paper,we propose a novel DRM scheme with secure key management and dynamic usage control in cloud computing.We present a secure key management mechanism based on attribute-based encryption and proxy re-encryption.Only the users whose attributes satisfy the access policy of the encrypted content and who have effective usage rights can be able to recover the content encryption key and further decrypt the content.The attribute based mechanism allows the content provider to selectively provide fine-grained access control of contents among a set of users,and also enables the license server to implement immediate attribute and user revocation.Moreover,our scheme supports privacy-preserving dynamic usage control based on additive homomorphic encryption,which allows the license server in the cloud to update the users' usage rights dynamically without disclosing the plaintext.Extensive analytical results indicate that our proposed scheme is secure and efficient.展开更多
The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the fi...The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the first research aimed at FPGA-based as well as GPGPU-based accelerator design.This paper quantitatively analyzes the workload,computational intensity and memory performance of a single-particle 3D reconstruction application,called EMAN,and parallelizes it on CUDA GPGPU architectures and decouples the memory operations from the computing flow and orchestrates the thread-data mapping to reduce the overhead of off-chip memory operations.Then it exploits the trend towards FPGA-based accelerator design,which is achieved by offloading computingintensive kernels to dedicated hardware modules.Furthermore,a customized memory subsystem is also designed to facilitate the decoupling and optimization of computing dominated data access patterns.This paper evaluates the proposed accelerator design strategies by comparing it with a parallelized program on a 4-cores CPU.The CUDA version on a GTX480 shows a speedup of about 6 times.The performance of the stream architecture implemented on a Xilinx Virtex LX330 FPGA is justified by the reported speedup of 2.54 times.Meanwhile,measured in terms of power efficiency,the FPGA-based accelerator outperforms a 4-cores CPU and a GTX480 by 7.3 times and 3.4 times,respectively.展开更多
基金ACKNOWLEDGEMENTS This work has been supported by the National Natural Science Foundation of China under Grant No. 61272519, 61121061.
文摘In order to achieve fine-grained access control in cloud computing,existing digital rights management(DRM) schemes adopt attribute-based encryption as the main encryption primitive.However,these schemes suffer from inefficiency and cannot support dynamic updating of usage rights stored in the cloud.In this paper,we propose a novel DRM scheme with secure key management and dynamic usage control in cloud computing.We present a secure key management mechanism based on attribute-based encryption and proxy re-encryption.Only the users whose attributes satisfy the access policy of the encrypted content and who have effective usage rights can be able to recover the content encryption key and further decrypt the content.The attribute based mechanism allows the content provider to selectively provide fine-grained access control of contents among a set of users,and also enables the license server to implement immediate attribute and user revocation.Moreover,our scheme supports privacy-preserving dynamic usage control based on additive homomorphic encryption,which allows the license server in the cloud to update the users' usage rights dynamically without disclosing the plaintext.Extensive analytical results indicate that our proposed scheme is secure and efficient.
基金Supported by the National Basic Research Program of China(No.2012CB316502)the National High Technology Research and DevelopmentProgram of China(No.2009AA01A129)the National Natural Science Foundation of China(No.60921002)
文摘The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the first research aimed at FPGA-based as well as GPGPU-based accelerator design.This paper quantitatively analyzes the workload,computational intensity and memory performance of a single-particle 3D reconstruction application,called EMAN,and parallelizes it on CUDA GPGPU architectures and decouples the memory operations from the computing flow and orchestrates the thread-data mapping to reduce the overhead of off-chip memory operations.Then it exploits the trend towards FPGA-based accelerator design,which is achieved by offloading computingintensive kernels to dedicated hardware modules.Furthermore,a customized memory subsystem is also designed to facilitate the decoupling and optimization of computing dominated data access patterns.This paper evaluates the proposed accelerator design strategies by comparing it with a parallelized program on a 4-cores CPU.The CUDA version on a GTX480 shows a speedup of about 6 times.The performance of the stream architecture implemented on a Xilinx Virtex LX330 FPGA is justified by the reported speedup of 2.54 times.Meanwhile,measured in terms of power efficiency,the FPGA-based accelerator outperforms a 4-cores CPU and a GTX480 by 7.3 times and 3.4 times,respectively.
