用知识蒸馏让加密无人机导航快18倍，兼顾安全与实时性。
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation
- 用知识蒸馏压缩加密模型，降低计算开销
- 加密状态下处理摄像头数据，速度提升18倍
- 适合部署在高安全要求的无人机任务中
为保障无人飞行器（UAV）等关键系统的安全，导航路径隐私保护至关重要。尽管强化学习（RL）与全同态加密（FHE）结合具有潜力，但FHE带来的高计算开销仍是主要挑战。本文提出一种创新方法，通过知识蒸馏提升安全无人机导航的实用性。框架融合RL与FHE，既抵御对抗攻击，又实现加密无人机图像的实时处理，确保数据安全。为缓解FHE延迟，采用知识蒸馏压缩网络，实现18倍加速，性能几乎无损：压缩模型的R²得分为0.9499，原模型为0.9631。该方法验证了在加密条件下执行无人机导航任务的可行性，兼顾安全性、性能效率与实时性，为敏感环境中自主无人机的部署提供支持。
原文摘要 · Abstract (English)
In safeguarding mission-critical systems, such as Unmanned Aerial Vehicles (UAVs), preserving the privacy of path trajectories during navigation is paramount. While the combination of Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) holds promise, the computational overhead of FHE presents a significant challenge. This paper proposes an innovative approach that leverages Knowledge Distillation to enhance the practicality of secure UAV navigation. By integrating RL and FHE, our framework addresses vulnerabilities to adversarial attacks while enabling real-time processing of encrypted UAV camera feeds, ensuring data security. To mitigate FHE's latency, Knowledge Distillation is employed to compress the network, resulting in an impressive 18x speedup without compromising performance, as evidenced by an R-squared score of 0.9499 compared to the original model's score of 0.9631. Our methodology underscores the feasibility of processing encrypted data for UAV navigation tasks, emphasizing security alongside performance efficiency and timely processing. These findings pave the way for deploying autonomous UAVs in sensitive environments, bolstering their resilience against potential security threats.
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