Theses and Dissertations

Xuhui Chen

Issuing Body

Mississippi State University

Advisor

Du, Qian (Jenny)

Committee Member

Fowler, James E.

Committee Member

Li, Pan

Date of Degree

12-11-2015

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

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 costeffective 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.

URI

https://hdl.handle.net/11668/18877

Comments

large scale||privacy-preserving||LSE||Cloud computing

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