Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud

Computation outsourcing to the cloud has become a popular application in the age of cloud computing. Recently, two protocols for secure outsourcing scientific computations, i.e., linear equation solving and linear programming solving, to the cloud were proposed. In this paper, we improve the work by...

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Vydáno v:Journal of parallel and distributed computing Ročník 74; číslo 3; s. 2141 - 2151
Hlavní autoři: Chen, Fei, Xiang, Tao, Yang, Yuanyuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Inc 01.03.2014
Elsevier
Témata:
Applied sciences
Cloud computing
Computation outsourcing
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Cryptography
Distributed computing
Exact sciences and technology
Information, signal and communications theory
Linear equation solving
Linear programming
Memory and file management (including protection and security)
Memory organisation. Data processing
Signal and communications theory
Software
Telecommunications and information theory
Cloud computing
Linear programming
Linear equation solving
Distributed computing
Computation outsourcing
Transmission protocol
Pseudorandom number
Subcontracting
Modeling
Homomorphic encryption
Multiparty protocol
Scientific computation
Confidentiality
Efficiency
Public key
Linear transformation
Outsourcing
Cryptography
Computer security
Age
Random number generation
ISSN:0743-7315, 1096-0848
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Popis
Shrnutí:Computation outsourcing to the cloud has become a popular application in the age of cloud computing. Recently, two protocols for secure outsourcing scientific computations, i.e., linear equation solving and linear programming solving, to the cloud were proposed. In this paper, we improve the work by proposing new protocols that achieve significant performance gains. For linear equation solving outsourcing, we achieve the improvement by proposing a completely new protocol. The new protocol employs some special linear transformations and there are no homomorphic encryptions and interactions between the client and the cloud, compared with the previous protocol. For linear programming outsourcing, we achieve the improvement by reformulating the linear programming problem in the standard and natural form. We also introduce a method to reduce the key size by using a pseudorandom number generator. The design of the newly proposed protocols also sheds some insight on constructing secure outsourcing protocols for other scientific computations. Comparisons between our protocols and the previous protocols are given, which demonstrate significant improvements of our proposed protocols. We also carry out numerical experiments to validate the efficiency of our protocols for secure linear equation solving and linear programming outsourcing. •Point out a problem in a previous linear equation outsourcing protocol.•Propose new and efficient protocols for linear equation solving and linear programming outsourcing to the cloud.•New protocols improve the performance of previous protocols significantly.•Experimental results validate the new protocols.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2013.11.007