Imperial College London
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ProfessorDenizGunduz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 6218d.gunduz Website

 
 
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Assistant

 

Ms Joan O'Brien +44 (0)20 7594 6316

 
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Location

 

1016Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mital:2022:10.1109/tit.2022.3158868,
author = {Mital, N and Ling, C and Gunduz, D},
doi = {10.1109/tit.2022.3158868},
journal = {IEEE Transactions on Information Theory},
pages = {1--1},
title = {Secure distributed matrix computation with discrete fourier transform},
url = {http://dx.doi.org/10.1109/tit.2022.3158868},
volume = {68},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We consider the problem of secure distributed matrix computation (SDMC), where a user queries a function of data matrices generated at distributed source nodes. We assume the availability of N honest but curious computation servers, which are connected to the sources, the user, and each other through orthogonal and reliable communication links. Our goal is to minimize the amount of data that must be transmitted from the sources to the servers, called the upload cost, while guaranteeing that no T colluding servers can learn any information about the source matrices, and the user cannot learn any information beyond the computation result. We first focus on secure distributed matrix multiplication (SDMM), considering two matrices, and propose a novel polynomial coding scheme using the properties of finite field discrete Fourier transform, which achieves an upload cost significantly lower than the existing results in the literature. We then generalize the proposed scheme to include straggler mitigation, and to the multiplication of multiple matrices while keeping the input matrices, the intermediate computation results, as well as the final result secure against any T colluding servers. We also consider a special case, called computation with own data, where the data matrices used for computation belong to the user. In this case, we drop the security requirement against the user, and show that the proposed scheme achieves the minimal upload cost. We then propose methods for performing other common matrix computations securely on distributed servers, including changing the parameters of secret sharing, matrix transpose, matrix exponentiation, solving a linear system, and matrix inversion, which are then used to show how arbitrary matrix polynomials can be computed securely on distributed servers using the proposed procedure
AU  - Mital,N
AU  - Ling,C
AU  - Gunduz,D
DO  - 10.1109/tit.2022.3158868
EP  - 1
PY  - 2022///
SN  - 0018-9448
SP  - 1
TI  - Secure distributed matrix computation with discrete fourier transform
T2  - IEEE Transactions on Information Theory
UR  - http://dx.doi.org/10.1109/tit.2022.3158868
UR  - https://ieeexplore.ieee.org/document/9732990
UR  - http://hdl.handle.net/10044/1/97825
VL  - 68
ER  -
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