Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance

TL;DR

This paper develops sparse secret sharing schemes for private distributed matrix multiplication that preserve matrix sparsity and provide strong privacy guarantees, balancing straggler tolerance and privacy degradation.

Contribution

It generalizes Shamir's secret sharing to multiple shares with fixed threshold, enhancing sparsity preservation and privacy in distributed matrix multiplication.

Findings

Schemes maintain sparsity of matrices during computation.

Increasing shares improves straggler tolerance but slightly reduces privacy.

Privacy degradation is negligible with a small number of shares relative to input size.

Abstract

This paper considers the problem of outsourcing the multiplication of two private and sparse matrices to untrusted workers. Secret sharing schemes can be used to tolerate stragglers and guarantee information-theoretic privacy of the matrices. However, traditional secret sharing schemes destroy all sparsity in the offloaded computational tasks. Since exploiting the sparse nature of matrices was shown to speed up the multiplication process, preserving the sparsity of the input matrices in the computational tasks sent to the workers is desirable. It was recently shown that sparsity can be guaranteed at the expense of a weaker privacy guarantee. Sparse secret sharing schemes with only two output shares were constructed. In this work, we construct sparse secret sharing schemes that generalize Shamir's secret sharing schemes for a fixed threshold $t=2$ and an arbitrarily large number of shares. We design our schemes to provide the strongest privacy guarantee given a desired sparsity of the shares under some mild assumptions. We show that increasing the number of shares, i.e., increasing straggler tolerance, incurs a degradation of the privacy guarantee. However, this degradation is negligible when the number of shares is comparably small to the cardinality of the input alphabet.