Title: Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation Show Chinese title

Title: 面向具有FHE感知知识蒸馏的安全无人机导航构建

Abstract: 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. Show Chinese abstract

Abstract: 在保护关键任务系统，如无人驾驶飞行器（UAV）时，在导航过程中保护路径轨迹的隐私至关重要。虽然强化学习（RL）和全同态加密（FHE）的结合具有前景，但FHE的计算开销带来了重大挑战。本文提出了一种创新方法，利用知识蒸馏来提高安全无人机导航的实用性。通过整合RL和FHE，我们的框架在应对对抗攻击的同时，实现了对加密无人机摄像头画面的实时处理，确保数据安全。为减轻FHE的延迟，采用知识蒸馏压缩网络，结果在不牺牲性能的情况下实现了惊人的18倍加速，与原始模型的0.9631相比，R平方得分达到了0.9499。我们的方法强调了对无人机导航任务进行加密数据处理的可行性，同时注重安全性和性能效率以及及时处理。这些发现为在敏感环境中部署自主无人机铺平了道路，增强了其应对潜在安全威胁的韧性。