移动云数据计算的选择性加密和面向组件的重复数据删除

Sejun Song, Baek-Young Choi, Daehee Kim
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引用次数: 9

摘要

随着智能设备的普及和使用应用的多样化,用户也希望像使用静态计算机一样随时随地方便地执行资源密集型任务。为了克服智能设备在处理、存储和功率方面的固有资源限制,新兴的协作移动云技术,如移动云计算(MCC)、移动边缘计算(MEC)和雾计算(FC),通过利用分布式和远程云资源,增强了智能设备的功能。然而,在协作计算环境中,智能设备之间对大数据处理和交换的需求被认为是一个重大挑战。一种有效的减少源设备上数据的技术对于节省网络带宽和存储空间至关重要。从而提高了数据处理开销,减少了智能设备间数据移动带来的安全漏洞。在本文中,我们设计并开发了一种新的选择性加密和面向组件的重复数据删除(SEACOD)应用程序,该应用程序实现了MCC服务的快速有效的数据加密和减少。具体来说,SEACOD有效地删除文件、电子邮件以及基于其结构利用对象级组件的图像中的冗余对象。它还通过根据分解的数据类型自适应地应用压缩和加密方法,有效地降低了移动设备上的总体加密开销。我们使用结构化文件的真实数据集进行的评估表明,所提出的方案实现了与可变块重复数据删除一样好的存储节省,同时与文件级或大型固定大小块级重复数据删除一样快。
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Selective encryption and component-oriented deduplication for mobile cloud data computing
As smart devices gain their popularity and usage applications become versatile, the users are also hoping to perform resource intensive tasks at anywhere and anytime as conveniently as using their static computers. To overcome the smart device's intrinsic resource limitations in processing, storage, and power, emerging collaborative mobile cloud technologies such as Mobile Cloud Computing (MCC), Mobile-Edge Computing (MEC), and Fog Computing (FC) augment the smart device's capabilities by leveraging distributed and remote cloud resources. However, in collaborative computing environments, the demand for big data processing and exchanges among smart devices is considered as a significant challenge. An effective technique to reduce data at a source device is essential to save network bandwidth and storage spaces. It, in turn, improves the data processing overhead as well as reduces the security vulnerability caused by data movement among the smart devices. In this paper, we design and develop a novel Selective Encryption and Component-Oriented Deduplication (SEACOD) application that achieves both fast and effective data encryption and reduction for MCC services. Specifically, SEACOD efficiently deduplicates redundant objects in files, emails, as well as images exploiting object-level components based on their structures. It also effectively reduces the overall encryption overhead on the mobile devices by adaptively applying compression and encryption methods according to the decomposed data types. Our evaluation using real datasets of structured files shows that the proposed scheme accomplishes as good of storage savings as a variable-block deduplication, while being as fast as a file-level or a large fixed-size block-level deduplication.
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