Securely Outsourcing Large Scale Eigen Value Problem to Public Cloud

TL;DR

This paper presents a secure protocol for outsourcing large scale eigenvalue computations to a public cloud, ensuring data privacy, result correctness, and robustness against malicious cloud behavior.

Contribution

It introduces a novel privacy-preserving protocol using matrix transformations and result verification for large scale eigenvalue problems in cloud computing.

Findings

High efficiency and correctness demonstrated through experiments

Strong security and robustness against malicious cloud attacks

Effective result verification mechanism for detecting cheating

Abstract

Cloud computing enables clients with limited computational power to economically outsource their large scale computations to a public cloud with huge computational power. Cloud has the massive storage, computational power and software which can be used by clients for reducing their computational overhead and storage limitation. But in case of outsourcing, privacy of client's confidential data must be maintained. We have designed a protocol for outsourcing large scale Eigen value problem to a malicious cloud which provides input/output data security, result verifiability and client's efficiency. As the direct computation method to find all eigenvectors is computationally expensive for large dimensionality, we have used power iterative method for finding the largest Eigen value and the corresponding Eigen vector of a matrix. For protecting the privacy, some transformations are applied to the input matrix to get encrypted matrix which is sent to the cloud and then decrypting the result that is returned from the cloud for getting the correct solution of Eigen value problem. We have also proposed result verification mechanism for detecting robust cheating and provided theoretical analysis and experimental result that describes high-efficiency, correctness, security and robust cheating resistance of the proposed protocol.