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STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
Pseudo-random generation from one-way functions
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
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A mathematical theory of communication
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Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Convex Optimization
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STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
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SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
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EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
A fully homomorphic encryption scheme
A fully homomorphic encryption scheme
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CRYPTO'10 Proceedings of the 30th annual conference on Advances in cryptology
Improved delegation of computation using fully homomorphic encryption
CRYPTO'10 Proceedings of the 30th annual conference on Advances in cryptology
Harnessing the Cloud for Securely Solving Large-Scale Systems of Linear Equations
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Efficient distributed linear programming with limited disclosure
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Verifiable delegation of computation over large datasets
CRYPTO'11 Proceedings of the 31st annual conference on Advances in cryptology
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
Mathematics of Public Key Cryptography
Mathematics of Public Key Cryptography
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TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of Linear Equations
IEEE Transactions on Parallel and Distributed Systems
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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.