Efficient Secure Outsourcing of Large-Scale Sparse Linear Systems of Equations - 2018


Solving large-scale sparse linear systems of equations (SLSEs) is one in all the foremost common and basic problems in massive knowledge, however it is very challenging for resource-restricted users. Cloud computing has been proposed as a timely, efficient, and cost-effective means of solving such expensive computing tasks. Nevertheless, one essential concern in cloud computing is data privacy. Specifically, clients' SLSEs sometimes contain personal data that ought to remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of linear systems of equations (LSEs) have high computational complexity, and do not exploit the sparsity in the LSEs. More importantly, they share a typical serious problem, i.e., a huge range of memory I/O operations. This downside has been largely neglected within the past, however after all is of particular importance and could eventually render those outsourcing schemes impractical. During this Project, we have a tendency to develop an efficient and sensible secure outsourcing algorithm for solving giant-scale SLSEs, that has low computational and memory I/O complexities and can protect shoppers' privacy well. We implement our algorithm on Amazon Elastic Compute Cloud, and realize that the proposed algorithm offers important time savings for the shopper (up to seventy four percent) compared to previous algorithms.

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