SERENE: A Collusion Resilient Replication-based Verification Framework
<|reference_start|>SERENE: A Collusion Resilient Replication-based Verification Framework: 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.<|reference_end|>
@article{esmaeili2024serene:,
    title={SERENE: A Collusion Resilient Replication-based Verification Framework},
    author={Amir Esmaeili, Abderrahmen Mtibaa},
    journal={arXiv preprint arXiv:2404.11410},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11410},
    primaryClass={cs.CR cs.NI}
}