Cloud computing aids users to outsource their data in the cloud remotely to prevent them from burdens of local storage and maintenance. Users no longer have possession and control of these data. This property brings m...
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Cloud computing aids users to outsource their data in the cloud remotely to prevent them from burdens of local storage and maintenance. Users no longer have possession and control of these data. This property brings many new security challenges like unauthorised entities and correctness of stored data. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. We propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the XOR operation and iris feature extraction as the strong factors. This work gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user's iris features to secure and integrate data in a manner difficult for any internal or external entity to take or compromise it. Extensive security and performance analysis show that our scheme is highly efficient and provably secure.
MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many use...
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ISBN:
(纸本)9781479966226
MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many users, it is an essential problem to preserve the privacy between different applications in the same network. In this paper, we propose SDPMN, a framework that using software defined network (SDN) to distinguish the network between each application, which is a manageable and scalable method. We design this framework based on the existing SDN structure and Hardtop networks. Since the rule space of each SDN device is limited, we also propose the rule placement optimization for this framework to maximize the hardware supported isolated application networks. We state this problem in a general MapReduce network and design a heuristic algorithm to find the solution. From the simulation based evaluation, with our algorithm, the given network can support more privacy preserving application networks with SDN switches.
Deterministic multithreading (DMT) system is well-known to eliminate the harmful program behaviors caused by nondeterminism, i.e., always proceeding the program execution into the same thread schedule for the same giv...
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Deterministic multithreading (DMT) system is well-known to eliminate the harmful program behaviors caused by nondeterminism, i.e., always proceeding the program execution into the same thread schedule for the same given input. To achieve this goal, two kinds of schedules are enforced by existing DMT systems. 1) A mem-based schedule ensures the determinism with the total order of the shared memory accesses, and 2) A sync-based schedule makes it by only enforcing the total order of the synchronization operations. Mem-schedule achieves full determinism but suffers from prohibitive overhead; while sync-schedule mitigates this overhead but cannot ensure the determinism for the race schedules, i.e., part determinism. Much recent research is devoted to the hybrid schedule combining the determinism of mem-schedule and efficiency of sync-schedule. However, they suffer from the practicability and scalability problems due to the defects of their technical characteristics, such as trace collection in advance and huge schedule memoization. To address the above problem, this paper proposes esDMT, an efficient and scalable DMT system using a new technique of memory isolation. It can improve the efficiency by proceeding the execution of each thread in parallel within its private virtual memory, and defers the determinism guarantee by updating private memory into shared memory in a deterministic order according to deterministic lock algorithm, thus further reducing the overhead of inter-thread waiting. In contrast to the previous hybrid work avoiding the nondeterminism of race schedules offline based on the enormous historical records, our key insight is to eliminate the nondeterminism of race schedules online at runtime. Our experimental results on PARSEC benchmarks show that esDMT eliminates the nondeterminism successfully, almost gains the same performance as the sync-schedule (with
The time-critical online contest environment calls for an elegant and precise management system to start a contest online. The management system should simultaneously follow three rules of security that are confidenti...
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The time-critical online contest environment calls for an elegant and precise management system to start a contest online. The management system should simultaneously follow three rules of security that are confidentiality, anonymity and fairness. We present in this paper a novel system named Secure Synchronized Reading (SSR) system as well as its corresponding security model according to the three rules. SSR can evoke a synchronized start for all competitors in a contest at the intended time by employing a Randomness-reused Identity-Based Encryption (RIBE) scheme. It can avoid huge differences among delivery delays of heterogeneous competitors and any false start by an adverse competitor. Consequently when an online contest begins, the SSR system performs quasi real-time with ignorable communication delays in the security model. As a complement, the analysis on the provable security of SSR is given finally, as well as a further analysis on the achievement of synchronization.
With the rapid growth of data, near-duplicate documents bearing high similarity are abundant. Elimination of near-duplicates can reduce storage cost and improve the quality of search indexes in data mining. A challeng...
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ISBN:
(纸本)9781479957125
With the rapid growth of data, near-duplicate documents bearing high similarity are abundant. Elimination of near-duplicates can reduce storage cost and improve the quality of search indexes in data mining. A challenging problem is to find near-duplicate records in large-scale collections efficiently. There have already been several efforts on implementing near-duplicate detection on different architectures. In this paper, a new implementation, using a special hash function namely simhash, is proposed to identify near-duplicate documents on CUDA enabled devices. Two mechanisms are designed to achieve higher performance, including swapping and dynamic allocating. Experimental results show that our parallel implementation outperforms the serial CPU version, achieving up to 18 times.
With the development of desktop virtualization technology, more and more companies begins to deploy virtual desktop. Traditional NAS and SAN storages can only support small-scale users due to network bandwidth limit a...
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Replica management has become a hot research topic in storage systems. This paper presents a dynamic replica management strategy based on response time, named RTRM. RTRM strategy consists of replica creation, replica ...
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CS (Conflict Serializability) is a recently proposed relaxer correctness criterion that can increase transactional memory's parallelism. DDA (Distributed Dependency-Aware) model is currently proposed to implement ...
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With the rapid development of intelligent video surveillance system for transportation, traditional single-camera-based video analysis has become insufficient. Many researches have focused on the non-overlapping multi...
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With the rapid development of intelligent video surveillance system for transportation, traditional single-camera-based video analysis has become insufficient. Many researches have focused on the non-overlapping multi-camera target tracking. However, the tracking precision and the computing overhead are still big obstacles. This paper proposes a novel vehicle tracking approach for non-overlapping multi-camera targets with data fusion by using minimum cost and maximum flow method. Structured information of moving targets is extracted and associated with other information such as motion time, the topology of camera network to solve targeted vehicle tracks. Besides, to improve the performance of the process, a parallelization algorithm for camera network topology partitioning is presented, which makes it possible for each sub-graph to track target independently in parallel. The experiment results show that the presented approach is able to perform target vehicle tracking analysis with high efficiency and accuracy.
Virtualization has become a prominent tool in data centers and is extensively leveraged in cloud environments: it enables multiple virtual machines (VMs) - with multiple operating systems and applications - to run wit...
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Virtualization has become a prominent tool in data centers and is extensively leveraged in cloud environments: it enables multiple virtual machines (VMs) - with multiple operating systems and applications - to run within a physical server. However, virtualization introduces the challenging issue of preserving the high disk utilization (i.e., reducing the seek delay and rotation overhead) when allocating disk resources to VMs. Exploiting spatial locality, a key technique for improving disk utilization and performance, faces additional challenges in the virtualized cloud because of the transparency feature of virtualization (hyper visors do not have the information about the access patterns of applications running within each VM). To this end, this paper contributes a novel disk I/O scheduling framework, named Pregather, to improve disk I/O efficiency through exposure and exploitation of the special spatial locality in the virtualized environment (regional and sub-regional spatial locality corresponds to the virtual disk space and applications' access patterns, respectively), thereby improving the performance of disk-intensive applications without harming the transparency feature of virtualization (without a priori knowledge of the applications' access patterns). The key idea behind Pregather is to implement an intelligent model to predict the access regularity of sub-regional spatial locality for each VM. We implement the Pregather disk scheduling framework and perform extensive experiments that involve multiple simultaneous applications of both synthetic benchmarks and a MapReduce application on Xen-based platforms. Our experiments demonstrate the accuracy of our prediction model and indicate that Pregather results in the high disk spatial locality and a significant improvement in disk throughput and application performance.
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