Ciphertext policy attribute based encryption(CP-ABE)can provide high finegrained access control for cloud storage.However,it needs to solve problems such as property privacy protection,ciphertext search and data updat...Ciphertext policy attribute based encryption(CP-ABE)can provide high finegrained access control for cloud storage.However,it needs to solve problems such as property privacy protection,ciphertext search and data update in the application process.Therefore,based on CP-ABE scheme,this paper proposes a dynamically updatable searchable encryption cloud storage(DUSECS)scheme.Using the characteristics of homomorphic encryption,the encrypted data is compared to achieve efficient hiding policy.Meanwhile,adopting linked list structure,the DUSECS scheme realizes the dynamic data update and integrity detection,and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm.The analysis of security and performance shows that the scheme is secure and efficient.展开更多
Cloud storage is getting more and more popular as a new trend of data management. Data replication has been widely used to increase the data availability in cloud storage systems. However,most data replication schemes...Cloud storage is getting more and more popular as a new trend of data management. Data replication has been widely used to increase the data availability in cloud storage systems. However,most data replication schemes do not fully consider cost and latency issues when users need large amounts of remote replicas. We present an improved dynamic replication management scheme( IDRMS). By adding a prediction model,the optimal allocation of replicas among the cloud storage nodes is determined that the total communication cost and network delay are minimal. When the local data block is frequently requested,the data replicas can be moved to a closer or cheaper node for cost reduction and increased efficiency. Moreover,we replace the B+tree with the B*tree to speed up the search and reduce workload with the lowest blocking probability. We define the value of popularity to adjust the placement of replicas dynamically. We divide the data nodes in the network into hot nodes and cool nodes. By changing to visit cool nodes instead of hot nodes,we can balance the workload in the network. Finally,we implement IDRMS in Matlab simulation platform and simulation results demonstrate that IDRMS outperforms other replication management schemes in terms of communication cost and load balancing for large-scale cloud storage.展开更多
基金supported by the National Nature Science Foundation of China under grant No.(61562059,61461027,61462060)。
文摘Ciphertext policy attribute based encryption(CP-ABE)can provide high finegrained access control for cloud storage.However,it needs to solve problems such as property privacy protection,ciphertext search and data update in the application process.Therefore,based on CP-ABE scheme,this paper proposes a dynamically updatable searchable encryption cloud storage(DUSECS)scheme.Using the characteristics of homomorphic encryption,the encrypted data is compared to achieve efficient hiding policy.Meanwhile,adopting linked list structure,the DUSECS scheme realizes the dynamic data update and integrity detection,and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm.The analysis of security and performance shows that the scheme is secure and efficient.
基金supported by the National Natural Science Foundation of China ( 61401234)
文摘Cloud storage is getting more and more popular as a new trend of data management. Data replication has been widely used to increase the data availability in cloud storage systems. However,most data replication schemes do not fully consider cost and latency issues when users need large amounts of remote replicas. We present an improved dynamic replication management scheme( IDRMS). By adding a prediction model,the optimal allocation of replicas among the cloud storage nodes is determined that the total communication cost and network delay are minimal. When the local data block is frequently requested,the data replicas can be moved to a closer or cheaper node for cost reduction and increased efficiency. Moreover,we replace the B+tree with the B*tree to speed up the search and reduce workload with the lowest blocking probability. We define the value of popularity to adjust the placement of replicas dynamically. We divide the data nodes in the network into hot nodes and cool nodes. By changing to visit cool nodes instead of hot nodes,we can balance the workload in the network. Finally,we implement IDRMS in Matlab simulation platform and simulation results demonstrate that IDRMS outperforms other replication management schemes in terms of communication cost and load balancing for large-scale cloud storage.
