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.

翻译：在保护任务关键系统（如无人机）时，导航过程中路径轨迹的隐私保护至关重要。虽然强化学习与全同态加密的结合具有应用前景，但全同态加密的计算开销构成了重大挑战。本文提出一种创新方法，利用知识蒸馏提升安全无人机导航的实用性。通过整合强化学习与全同态加密，我们的框架在应对对抗性攻击脆弱性的同时，实现了加密无人机摄像数据的实时处理，确保数据安全。为缓解全同态加密的延迟问题，我们采用知识蒸馏技术压缩网络，在保持性能的前提下实现了18倍的显著加速——蒸馏后模型的决定系数为0.9499，与原始模型的0.9631相比性能相当。本方法论证了在无人机导航任务中处理加密数据的可行性，在保障安全性的同时兼顾性能效率与实时处理。这些发现为在敏感环境中部署自主无人机奠定了基础，增强了其抵御潜在安全威胁的能力。