Affiliations: [a] Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India
| [b] Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India
Correspondence:
[*]
Corresponding author. Malay Kumar, Department of Computer Science and Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, India. Tel.: +91 8959596477; Fax: +91 771 2254600; E-mail: mkumar.phd2014.cs@nitrr.ac.in.
Abstract: Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.
Keywords: Regression analysis, computation outsourcing, cloud computing, linear transformation
