Learning-Augmented Perfectly Secure Collaborative Matrix Multiplication

TL;DR

This paper introduces a new perfectly secure matrix multiplication protocol for multiparty computation that guarantees privacy, correctness, and optimal recovery thresholds, and enhances efficiency with a learning-augmented approach.

Contribution

It develops a novel secure MPC scheme using polynomial encoding and introduces a learning-augmented extension with tensor decomposition for improved scalability.

Findings

Achieves perfect secrecy with optimal recovery threshold.

Provides up to 80% computational efficiency gains with learning methods.

Ensures privacy and correctness in multiparty matrix multiplication.

Abstract

This paper presents a perfectly secure matrix multiplication (PSMM) protocol for multiparty computation (MPC) of $\mathrm{A}^{\top}\mathrm{B}$ over finite fields. The proposed scheme guarantees correctness and information-theoretic privacy against threshold-bounded, semi-honest colluding agents, under explicit local storage constraints. Our scheme encodes submatrices as evaluations of sparse masking polynomials and combines coefficient alignment with Beaver-style randomness to ensure perfect secrecy. We demonstrate that any colluding set of parties below the security threshold observes uniformly random shares, and that the recovery threshold is optimal, matching existing information-theoretic limits. Building on this framework, we introduce a learning-augmented extension that integrates tensor-decomposition-based local block multiplication, capturing both classical and learned low-rank methods. We demonstrate that the proposed learning-based PSMM preserves privacy and recovery guarantees for MPC, while providing scalable computational efficiency gains (up to $80\%$) as the matrix dimensions grow.