SERENE: A Collusion Resilient Replication-based Verification Framework

TL;DR

SERENE is a novel framework that enhances replication-based verification for autonomous driving by effectively detecting and mitigating colluding malicious workers using a lightweight, single-task detection algorithm.

Contribution

It introduces a collusion-resilient verification framework that outperforms existing methods in detection and mitigation accuracy without relying on trusted third parties.

Findings

50% improvement in detection accuracy

60% improvement in mitigation accuracy

Effective against colluding malicious workers

Abstract

The rapid advancement of autonomous driving technology is accompanied by substantial challenges, particularly the reliance on remote task execution without ensuring a reliable and accurate returned results. This reliance on external compute servers, which may be malicious or rogue, represents a major security threat. While researchers have been exploring verifiable computing, and replication-based task verification as a simple, fast, and dependable method to assess the correctness of results. However, colluding malicious workers can easily defeat this method. Existing collusion detection and mitigation solutions often require the use of a trusted third party server or verified tasks which may be hard to guarantee, or solutions that assume the presence of a minority of colluding servers. We propose SERENE, a collusion resilient replication-based verification framework that detects, and mitigates colluding workers. Unlike state-of-the-art solutions, SERENE uses a lightweight detection algorithm that detects collusion based on a single verification task. Mitigation requires a two stage process to group the workers and identifying colluding from honest workers. We implement and compare SERENE's performance to Staab et. al, resulting in an average of 50\% and 60\% accuracy improvement in detection and mitigation accuracy respectively.