# Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation

arXiv: https://arxiv.org/abs/2411.00403 · HTML: https://mlsanity.com/paper/2411.00403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE](https://mlsanity.com/paper/2310.08012/) (2023)
- [A Survey on Security and Privacy Issues of UAVs](https://mlsanity.com/paper/2109.14442/) (2021)
- [HEAAN Demystified: Accelerating Fully Homomorphic Encryption Through Architecture-centric Analysis and Optimization](https://mlsanity.com/paper/2003.04510/) (2020)
- [Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey](https://mlsanity.com/paper/2105.01347/) (2021)
- [SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks](https://mlsanity.com/paper/2211.11321/) (2022)
- [EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV](https://mlsanity.com/paper/2403.05422/) (2024)
- [Enhancing Privacy and Security of Autonomous UAV Navigation](https://mlsanity.com/paper/2404.17225/) (2024)

## Abstract (arXiv)

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.