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"In This Application economically enables customers with limited computational
resources to outsource large-scale computations to the Transient Server . However,
how to protect customers’ confidential data involved in the computations
then becomes a major security concern. In this paper, we present a secure outsourcing
mechanism for solving large-scale systems of linear equations (LE) in
Transient Server . Because applying traditional approaches like Gaussian elimination
or LU decomposition (aka. direct method) to such large-scale LEs would
be prohibitively expensive, we build the secure LE outsourcing mechanism via a
completely different approach—iterative method, which is much easier to implement
in practice and only demands relatively simpler matrix-vector operations.
Specifically, our mechanism enables a customer to securely harness the Transient
Server for iteratively finding successive approximations to the LE solution,
while keeping both the sensitive input and output of the computation private. For
robust cheating detection, we further explore the algebraic property of matrixvector
operations and propose an efficient result verification mechanism, which
allows the customer to verify all answers received from previous iterative
approximations in one batch with high probability. Thorough security analysis
and prototype experiments on Amazon EC2 demonstrate the validity and practicality
of our proposed design." www.fu-vision .com
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