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In this work, we consider the problem of secure multiparty computation ...
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GCSA Codes with Noise Alignment for Secure Coded MultiParty Batch Matrix Multiplication
A secure multiparty batch matrix multiplication problem (SMBMM) is cons...
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Private Secure Coded Computation
We introduce a variation of coded computation that ensures data security...
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Analog Lagrange Coded Computing
A distributed computing scenario is considered, where the computational ...
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Distributed and Private Coded Matrix Computation with Flexible Communication Load
Tensor operations, such as matrix multiplication, are central to larges...
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ParityChecked Strassen Algorithm
To multiply astronomic matrices using parallel workers subject to stragg...
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Secrecy: Secure collaborative analytics on secretshared data
We study the problem of composing and optimizing relational query plans ...
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Secure Coded MultiParty Computation for Massive Matrix Operations
In this paper, we consider a secure multiparty computation problem (MPC), where the goal is to offload the computation of an arbitrary polynomial function of some massive private matrices to a network of workers. The workers are not reliable, some may collude to gain information about the input data. The system is initialized by sharing a (randomized) function of each input matrix to each server. Since the input matrices are massive, the size of each share is assumed to be at most 1/k fraction of the input matrix, for some k ∈N. The objective is to minimize the number of workers needed to perform the computation task correctly, such that even if an arbitrary subset of t1 workers, for some t∈N, collude, they cannot gain any information about the input matrices. We propose a sharing scheme, called polynomial sharing, and show that it admits basic operations such as adding and multiplication of matrices, and transposing a matrix. By concatenating the procedures for basic operations, we show that any polynomial function of the input matrices can be calculated, subject to the problem constraints. We show that the proposed scheme can offer orderwise gain, in terms of number of workers needed, compared to the approaches formed by concatenation of job splitting and conventional MPC approaches.
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