PolyDot Coded Privacy Preserving Multi-Party Computation at the Edge

TL;DR

This paper introduces PolyDot-CMPC, a novel coded multi-party computation algorithm that leverages PolyDot codes and garbage terms to improve efficiency in privacy-preserving distributed matrix multiplication at the edge.

Contribution

The paper proposes a new CMPC algorithm using PolyDot codes that reduces worker requirements by exploiting polynomial garbage terms, outperforming entangled polynomial codes in MPC.

Findings

PolyDot-CMPC reduces the number of workers needed for privacy-preserving matrix multiplication.

Exploiting garbage terms in polynomial construction enhances efficiency in coded MPC.

PolyDot-CMPC outperforms entangled polynomial codes in the MPC setting.

Abstract

We investigate the problem of privacy preserving distributed matrix multiplication in edge networks using multi-party computation (MPC). Coded multi-party computation (CMPC) is an emerging approach to reduce the required number of workers in MPC by employing coded computation. Existing CMPC approaches usually combine coded computation algorithms designed for efficient matrix multiplication with MPC. We show that this approach is not efficient. We design a novel CMPC algorithm; PolyDot coded MPC (PolyDot-CMPC) by using a recently proposed coded computation algorithm; PolyDot codes. We exploit "garbage terms" that naturally arise when polynomials are constructed in the design of PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. We show that entangled polynomial codes, which are consistently better than PolyDot codes in coded computation setup, are not necessarily better than PolyDot-CMPC in MPC setting.