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Collusion-Resilient Replication-based Verification Framework for Secure Outsourced Computation in Autonomous Driving


Core Concepts
SERENE, a collusion-resilient replication-based verification framework, can accurately detect and mitigate collusion attacks by malicious workers even when they represent the majority in the network, without relying on trusted third parties or pre-verified tasks.
Abstract
The paper proposes SERENE, a collusion-resilient replication-based verification framework for secure outsourced computation in autonomous driving systems. Key highlights: Autonomous driving systems rely heavily on outsourced computation, which raises security concerns due to the potential for malicious or colluding workers to return incorrect results. Existing replication-based verification methods are vulnerable to collusion attacks, where malicious workers coordinate to submit the same incorrect results and defeat the majority voting scheme. SERENE introduces a two-phase approach to detect and mitigate collusion: The detection module identifies the presence of collusion by detecting two groups of workers consistently disagreeing on task results. The mitigation module partitions the workers into two groups, identifies the colluding group, and isolates the colluding workers. SERENE's detection algorithm is lightweight and can accurately identify collusion even when colluding workers represent up to 90% of the worker population, without relying on trusted third parties or pre-verified tasks. Evaluation results show that SERENE outperforms the state-of-the-art SnE algorithm, achieving up to 50% and 60% improvements in detection and mitigation accuracy, respectively. SERENE also runs 2x faster than SnE while incurring slightly higher resource utilization.
Stats
The paper does not provide specific numerical data to support the key logics. The evaluation section presents performance metrics comparing SERENE and SnE, such as detection delay, detection accuracy, mitigation accuracy, and mitigation latency.
Quotes
The paper does not contain any striking quotes that support the key logics.

Deeper Inquiries

How can SERENE's detection and mitigation algorithms be extended to handle dynamic worker populations, where nodes join and leave the network over time

SERENE's detection and mitigation algorithms can be extended to handle dynamic worker populations by incorporating dynamic graph analysis techniques. When nodes join or leave the network, the graph structure representing the workers can be updated in real-time. Nodes that have not been probed for a certain period can be removed from the graph to ensure the accuracy of the analysis. Additionally, SERENE can implement algorithms that dynamically adjust the detection and mitigation processes based on the changing network topology. By continuously monitoring the network and adapting to node movements, SERENE can effectively handle dynamic worker populations.

What are the potential limitations or vulnerabilities of SERENE's approach if the colluding workers employ more sophisticated strategies beyond simply returning the same incorrect results

While SERENE's approach is robust in detecting and mitigating collusion among workers, there are potential limitations and vulnerabilities if colluding workers employ more sophisticated strategies. If colluding workers use strategies to mimic honest behavior or vary their collusion patterns, SERENE may face challenges in accurately identifying and isolating them. Advanced collusion techniques such as adaptive collusion, where nodes dynamically change their collusion behavior based on detection mechanisms, could potentially evade SERENE's detection algorithms. To address this, SERENE may need to incorporate machine learning algorithms or anomaly detection techniques to adapt to evolving collusion strategies and enhance its resilience against sophisticated attacks.

Can SERENE's techniques be applied to other types of outsourced computation beyond autonomous driving, such as cloud-based machine learning or scientific computing

SERENE's techniques can be applied to various types of outsourced computation beyond autonomous driving, such as cloud-based machine learning or scientific computing. To adapt SERENE for these applications, certain modifications may be required. For cloud-based machine learning, SERENE can be tailored to verify the correctness of model training tasks outsourced to cloud servers. This would involve adjusting the verification tasks and detection algorithms to suit the specific requirements of machine learning workflows. Similarly, for scientific computing, SERENE can be customized to ensure the accuracy of complex computational simulations performed on distributed systems. By tailoring the detection and mitigation processes to the unique characteristics of each application domain, SERENE can effectively enhance the security and reliability of outsourced computation in diverse fields.
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