Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation
This work addresses privacy and real-time processing for UAV navigation in sensitive environments, representing an incremental improvement by applying knowledge distillation to mitigate FHE latency.
The paper tackled the challenge of computational overhead in secure UAV navigation by combining Reinforcement Learning and Fully Homomorphic Encryption, achieving an 18x speedup with minimal performance loss (R-squared score of 0.9499 vs. 0.9631).
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.