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Secure cloud computing for solving large-scale linear systems of equations

Author: Xuhui Chen; Mississippi State University,; Mississippi State University. Department of Electrical and Computer Engineering,
Publisher: Mississippi State : Mississippi State University, 2015.
Dissertation: Thesis (M.S.) Mississippi State University. Department of Electrical and Computer Engineering 2015.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and cost effective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have  Read more...
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Details

Genre/Form: Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Xuhui Chen; Mississippi State University,; Mississippi State University. Department of Electrical and Computer Engineering,
OCLC Number: 934598834
Description: 1 online resource (vii, 37 pages) : color illustrations
Details: Mode of access: Internet via the World Wide Web.; System requirements: Internet connectivity; World Wide Web browser software; Adobe Acrobat Reader.
Responsibility: by Xuhui Chen.
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Abstract:

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and cost effective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms.

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