In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all...In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.展开更多
The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic ...The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.展开更多
Integration of the cloud desktop and cloud storage platform is urgent for enterprises. However, current proposals for cloud disk are not satisfactory in terms of the decoupling of virtual computing and business data s...Integration of the cloud desktop and cloud storage platform is urgent for enterprises. However, current proposals for cloud disk are not satisfactory in terms of the decoupling of virtual computing and business data storage in the cloud desktop environment. In this paper, we present a new virtual disk mapping method for cloud desktop storage. In Windows, compared with virtual hard disk method of popular cloud disks, the proposed implementation of client based on the virtual disk driver and the file system filter driver is available for widespread desktop environments, especially for the cloud desktop with limited storage resources. Further more, our method supports customizable local cache storage, resulting in userfriendly experience for thinclients of the cloud desktop. The evaluation results show that our virtual disk mapping method performs well in the readwrite throughput of different scale files.展开更多
Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a m...Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improv- ing quality of services. Many good approaches have been pro- posed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request dis- tributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication, in this paper, we propose Cloud Cache （C2）, an adaptive and scal- able load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balanc- ing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C2 runs as follows： 1） to run adaptive cache diffusion first, if a node is overloaded, load- shedding will be used; otherwise, load-stealing will be used; and 2） to run adaptive replication scheme second, if there is a very popular metadata item （or at least two items） causing a node be overloaded, adaptive replication scheme will be used,in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack prop- erty. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.展开更多
面向企业网或校园网的移动办公与存储的网盘系统有着广泛的市场需求,传统的网盘技术在性能、用户共享、安全性、可扩展性等方面存在诸多缺陷。针对这些不足,本文提出了一种基于云存储的高性能网盘系统架构:采用分布式文件系统MooseFS实...面向企业网或校园网的移动办公与存储的网盘系统有着广泛的市场需求,传统的网盘技术在性能、用户共享、安全性、可扩展性等方面存在诸多缺陷。针对这些不足,本文提出了一种基于云存储的高性能网盘系统架构:采用分布式文件系统MooseFS实现用户数据存储与访问的集群架构;在安全性方面,结合SAMBA实现用户权限管理,用户数据存储支持128 bit AES加密,SSH保证了传输链路的安全;最后,结合用户的实际需求,提供基于Web的访问方式以及客户端的同步盘模式。结果表明,系统在性能、安全性、可扩展性等多方面具有显著优势。展开更多
基金supported partly by the following funding agencies:the National Natural Science Foundation(No.61170274)the Innovative Research Groups of the National Natural Science Foundation(No.61121061)+1 种基金the National Key Basic Research Program of China (No.2011CB302506)Youth Scientific Research and Innovation Plan of Beijing University of Posts and Telecommunications(No. 2013RC1101)
文摘In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2015AA016006)the National Natural Science Foundation of China(60903220)
文摘The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.
基金key technologies of the integration of cloud desktop and cloud storage Platform is supported by ZTE Industry-Academia-Research Cooperation Funds
文摘Integration of the cloud desktop and cloud storage platform is urgent for enterprises. However, current proposals for cloud disk are not satisfactory in terms of the decoupling of virtual computing and business data storage in the cloud desktop environment. In this paper, we present a new virtual disk mapping method for cloud desktop storage. In Windows, compared with virtual hard disk method of popular cloud disks, the proposed implementation of client based on the virtual disk driver and the file system filter driver is available for widespread desktop environments, especially for the cloud desktop with limited storage resources. Further more, our method supports customizable local cache storage, resulting in userfriendly experience for thinclients of the cloud desktop. The evaluation results show that our virtual disk mapping method performs well in the readwrite throughput of different scale files.
文摘Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improv- ing quality of services. Many good approaches have been pro- posed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request dis- tributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication, in this paper, we propose Cloud Cache （C2）, an adaptive and scal- able load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balanc- ing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C2 runs as follows： 1） to run adaptive cache diffusion first, if a node is overloaded, load- shedding will be used; otherwise, load-stealing will be used; and 2） to run adaptive replication scheme second, if there is a very popular metadata item （or at least two items） causing a node be overloaded, adaptive replication scheme will be used,in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack prop- erty. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.
文摘面向企业网或校园网的移动办公与存储的网盘系统有着广泛的市场需求,传统的网盘技术在性能、用户共享、安全性、可扩展性等方面存在诸多缺陷。针对这些不足,本文提出了一种基于云存储的高性能网盘系统架构:采用分布式文件系统MooseFS实现用户数据存储与访问的集群架构;在安全性方面,结合SAMBA实现用户权限管理,用户数据存储支持128 bit AES加密,SSH保证了传输链路的安全;最后,结合用户的实际需求,提供基于Web的访问方式以及客户端的同步盘模式。结果表明,系统在性能、安全性、可扩展性等多方面具有显著优势。
