Belangrijkste concepten
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.
Samenvatting
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.
Statistieken
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.
Citaten
The paper does not contain any striking quotes that support the key logics.