# Enhancing Privacy and Security of Autonomous UAV Navigation

arXiv: https://arxiv.org/abs/2404.17225 · HTML: https://mlsanity.com/paper/2404.17225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [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)

## Built on by (most cited first, 1 of 1)

- [Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation](https://mlsanity.com/paper/2411.00403/) (2024)

## Abstract (arXiv)

Autonomous Unmanned Aerial Vehicles (UAVs) have become essential tools in defense, law enforcement, disaster response, and product delivery. These autonomous navigation systems require a wireless communication network, and of late are deep learning based. In critical scenarios such as border protection or disaster response, ensuring the secure navigation of autonomous UAVs is paramount. But, these autonomous UAVs are susceptible to adversarial attacks through the communication network or the deep learning models - eavesdropping / man-in-the-middle / membership inference / reconstruction. To address this susceptibility, we propose an innovative approach that combines Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) for secure autonomous UAV navigation. This end-to-end secure framework is designed for real-time video feeds captured by UAV cameras and utilizes FHE to perform inference on encrypted input images. While FHE allows computations on encrypted data, certain computational operators are yet to be implemented. Convolutional neural networks, fully connected neural networks, activation functions and OpenAI Gym Library are meticulously adapted to the FHE domain to enable encrypted data processing. We demonstrate the efficacy of our proposed approach through extensive experimentation. Our proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance.