PolyDot Coded Privacy Preserving Multi-Party Computation at the Edge

Abstract

Multi-party computation (MPC) is promising for privacy-preserving machine learning algorithms at edge networks, like federated learning. Despite their potential, existing MPC algorithms fail short of adapting to the limited resources of edge devices. A promising solution, and the focus of this work, is coded computation, which advocates the use of error-correcting codes to improve the performance of distributed computing through "smart" data redundancy. In this paper, we focus on coded privacy-preserving computation using Shamir's secret sharing. In particular, we design novel coded privacy-preserving computation mechanisms; MatDot coded MPC (MatDot-CMPC) and PolyDot coded MPC (PolyDot-CMPC) by employing recently proposed coded computation algorithms; MatDot and PolyDot. We take advantage of the "garbage terms" that naturally arise when polynomials are constructed in the design of MatDot-CMPC and PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. Also, we analyze MatDot-CMPC and PolyDot-CMPC in terms of their computation, storage, communication overhead as well as recovery threshold, so they can easily adapt to the limited resources of edge devices.