Verifiable Coded Computation of Multiple Functions

TL;DR

This paper extends coded computation techniques to evaluate multiple multivariate polynomials over large datasets in distributed systems, ensuring robustness, security, and verification against malicious or slow workers.

Contribution

It generalizes the Lagrange Coded Computing framework to handle multiple functions simultaneously with added security and verification features.

Findings

Reduces download cost with minimal computation overhead.

Provides robustness against stragglers and adversarial workers.

Includes verification schemes to detect malicious responses.

Abstract

We consider the problem of evaluating distinct multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its corresponding dataset and propose a generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to perform all computations simultaneously while providing robustness against stragglers who do not respond in time, adversarial workers who respond with wrong computation and information-theoretic security of dataset against colluding workers. Our scheme introduces a small computation overhead which results in a reduction in download cost and also offers comparable resistance to stragglers over existing solutions. On top of it, we also propose two verification schemes to detect the presence of adversaries, which leads to incorrect results, without involving additional nodes.