DocumentCode
3753293
Title
Enabling Efficient and Secure Outsourcing of Large Matrix Multiplications
Author
Kun Jia;Hongwei Li;Dongxiao Liu;Shui Yu
Author_Institution
Sch. of Comput. Sci. &
fYear
2015
Firstpage
1
Lastpage
6
Abstract
With the growing popularity of cloud computing, outsourced computing has attracted much research effort recently. A computationally weak client is capable of delegating its heavy computing tasks, such as large matrix multiplications, to the cloud server. Critical requirements for such tasks include the need to guarantee the unforgeability of computing results and the preservation of the privacy of clients. On one hand, the result computed by the cloud server needs to be verified since the cloud server cannot be fully honest. On the other hand, as the data involved in computing may contain some sensitive information of the client, the data should not be identified by the cloud server. In this paper, we address these above issues by developing an Efficient and Secure Outsourcing scheme for Large Matrix Multiplication, named ESO- LMM. Security analysis demonstrates that ESO-LMM achieves the security requirements in terms of unforgeability of proof and privacy protection of outsourced data. Furthermore, performance evaluation indicates that ESO-LMM is much more efficient compared with the existing works in terms of computation, communication and storage overhead.
Keywords
"Servers","Cloud computing","Security","Data privacy","Outsourcing","Computational modeling","Data preprocessing"
Publisher
ieee
Conference_Titel
Global Communications Conference (GLOBECOM), 2015 IEEE
Type
conf
DOI
10.1109/GLOCOM.2015.7417184
Filename
7417184
Link To Document