SERENE: A Collusion Resilient Replication-based Verification Framework

• URL: http://arxiv.org/abs/2404.11410v2
• Date: Thu, 18 Apr 2024 15:30:51 GMT
• Title: SERENE: A Collusion Resilient Replication-based Verification Framework
• Authors: Amir Esmaeili, Abderrahmen Mtibaa,
• Abstract summary: Collusion detection and mitigation solutions often require the use of a trusted third party server or verified tasks. We propose SERENE, a collusion resilient replication-based verification framework that detects, and mitigates colluding 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.
• Score: 0.4297070083645048
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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