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Linear programming (LP) has been well studied in the scientific community for various engineering applications such as network flow problems, packet routing, portfolio optimization, and financial data management, etc. In this paper, we first utilize the sparse matrix to investigate secure outsourcing for large-scale LP systems, which is considered as a prohibitively expensive computation for the clients...
We give protocols for the secure and private outsourcing of linear algebra computations, that enable a client to securely outsource expensive algebraic computations (like the multiplication of huge matrices) to two remote servers, such that the servers learn nothing about the customer's private input or the result of the computation,and any attempted corruption of the answer by the servers is detected...
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