Privacy-Preserving Polynomial Computing Over Distributed Data

TL;DR

This paper introduces a privacy-preserving method for polynomial computation over distributed data using Lagrange encoding, resilient to stragglers, byzantine workers, and collusion.

Contribution

It proposes a novel Lagrange encoding-based approach that ensures data security and robustness in distributed polynomial computations.

Findings

Resilient to stragglers and byzantine workers

Maintains data privacy against colluding workers

Effective in distributed polynomial computing scenarios

Abstract

In this letter, we delve into a scenario where a user aims to compute polynomial functions using their own data as well as data obtained from distributed sources. To accomplish this, the user enlists the assistance of $N$ distributed workers, thereby defining a problem we refer to as privacy-preserving polynomial computing over distributed data. To address this challenge, we propose an approach founded upon Lagrange encoding. Our method not only possesses the ability to withstand the presence of stragglers and byzantine workers but also ensures the preservation of security. Specifically, even if a coalition of $X$ workers collude, they are unable to acquire any knowledge pertaining to the data originating from the distributed sources or the user.