Enhancing Privacy and Security of Autonomous UAV Navigation (2404.17225v1)

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.

• The paper demonstrates that combining Reinforcement Learning with Fully Homomorphic Encryption secures UAV navigation with negligible performance loss.
• It adapts RL models to process encrypted data using the CKKS scheme and efficient image packing for faster computations.
• Experiments achieved a high R-squared score of 0.9631 and minimal mean absolute error, validating the method’s reliability.

Enhancing Privacy and Security of Autonomous UAV Navigation

Introduction

Autonomous drones, or Unmanned Aerial Vehicles (UAVs), are becoming more commonplace in industries like defense, disaster response, and product delivery. With the increasing reliance on them for critical tasks, ensuring their secure operation has become a primary concern. Think about it: these drones often work in high-stakes environments where an attack on the communication network could result in severe repercussions. This paper dives into one proposed solution, combining Fully Homomorphic Encryption (FHE) and Reinforcement Learning (RL) to secure UAV navigation.

Key Concepts

Let's unpack some of the key concepts used in this research for better clarity:

• Fully Homomorphic Encryption (FHE): This cryptographic method allows computations on encrypted data without decrypting it. It ensures that sensitive data remains secure even during processing.
• Reinforcement Learning (RL): A type of machine learning where agents learn to make decisions by performing actions and receiving feedback through rewards or penalties.

Challenges in UAV Navigation

Autonomous drones face several security threats, such as:

• Eavesdropping: Intercepting communication between the drone and its server.
• Man-in-the-middle attacks: Interfering with the communication to alter or inject messages.
• Adversarial attacks on deep learning models: Exploiting model vulnerabilities to infer sensitive information or to manipulate the model’s behavior.

The Proposed Method

The researchers propose an innovative approach that marries RL and FHE to create a secure and efficient UAV navigation system. The idea is to keep the data encrypted throughout the navigation process, such that only authorized persons can decrypt and understand it.

To implement this approach:

1. Adapt RL Models for Encrypted Data: The authors successfully adapted various components of RL models—convolutional layers, activation functions, and more—so they could process encrypted data.
2. Use CKKS Scheme: This encryption scheme supports operations on floating-point numbers, making it compatible with the UAV's navigation data.
3. Pack Input Images for Efficiency: The input images (e.g., from the drone’s camera) are encrypted in an efficient way to leverage SIMD (Single Instruction, Multiple Data) operations, speeding up the process.

Experimental Results

The authors performed an extensive set of experiments to validate their approach. Here are some key results:

• Negligible Performance Loss: The mean absolute error (MAE) between the encrypted system's outputs and the plaintext system’s outputs was minimal. This shows that encryption does not significantly affect the reliability or accuracy of the drone's navigation.
• R-squared Score: An impressively high R-squared score of 0.9631 validated the method’s accuracy when compared to the plaintext approach.

Practical Implications and Future Directions

Implementing FHE in UAV systems can drastically improve their security profiles. In environments like border surveillance or disaster relief, where data confidentiality is paramount, this approach ensures that data remains private and secure.

Looking ahead, one notable downside of FHE is its computational cost, which is relatively high. Future research will likely focus on optimizing these computations to make real-time encrypted processing more feasible. Additionally, the development of more efficient homomorphic encryption schemes and hardware acceleration could significantly reduce inference times.

Conclusion

This research provides a valuable contribution to the field of secure autonomous systems. By combining RL and FHE, the authors present a robust method to enhance the privacy and security of UAV navigation, ensuring that sensitive data remains secure without compromising on performance.

This work highlights promising avenues for further research, aiming to make secure autonomous UAVs practical and efficient for real-world applications.