Large matrix multiplications commonly take place in large-scale machine-learning
applications. Often, the sheer size of these matrices prevent carrying out the multiplication at …

We consider the problem of designing secure and private codes for distributed matrix-matrix
multiplication. A master server owns two private matrices and hires worker nodes to help …

We consider the problem of secure distributed matrix multiplication (SDMM), where a user
has two matrices and wishes to compute their product with the help of N honest but curious …

In this paper, we propose a novel construction for secure distributed matrix multiplication
(SDMM) based on algebraic geometry (AG) codes, which we call the PoleGap SDMM …

The paradigm-shifting developments of cryptography and information theory have focused
on the privacy of data-sharing systems, such as epidemiological studies, where agencies …

We introduce two generalizations to the paradigm of using Random Khatri-Rao Product
(RKRP) codes for distributed matrix multiplication. We first introduce a class of codes called …

This work investigates the design of sparse secret sharing schemes that encode a sparse
private matrix into sparse shares. This investigation is motivated by distributed computing …

We study the secure distributed matrix multiplication (SDMM) problem under arbitrary
collusion pattern. In the one-sided SDMM problem, where only one matrix of the matrix …

Distributed computing has made it possible to satisfy the demands for large-scale matrix
multiplication. A distributed computing system suffers from both straggler problem and …

Supporting multiple partial computations efficiently at each of the workers is a keystone in
distributed coded computing in order to speed up computations and to fully exploit the …