Enhancing Privacy and Security of Autonomous UAV Navigation

TL;DR

This paper introduces a novel framework combining Reinforcement Learning and Fully Homomorphic Encryption to enhance the security and privacy of autonomous UAV navigation, enabling encrypted real-time video processing with minimal performance loss.

Contribution

It presents an innovative end-to-end secure navigation system for UAVs using FHE and RL, addressing vulnerabilities in deep learning-based autonomous systems.

Findings

Secure UAV navigation with encrypted video feeds demonstrated

Negligible performance loss compared to non-encrypted systems

Effective adaptation of neural network components to FHE domain

Abstract

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.