Konsep Inti
A novel end-to-end framework combining Reinforcement Learning and Fully Homomorphic Encryption to enable secure and private autonomous UAV navigation.
Abstrak
The paper proposes an innovative approach that combines Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) to enable secure and private autonomous UAV navigation. The key highlights are:
Autonomous UAVs are susceptible to various adversarial attacks through the communication network or the deep learning models, such as eavesdropping, man-in-the-middle, membership inference, and reconstruction attacks.
To address this vulnerability, the authors develop an end-to-end secure framework that utilizes FHE to perform inference on encrypted input images captured by the UAV cameras.
Since FHE has limitations in implementing certain mathematical operations, the authors meticulously adapt the components of the RL model, including convolutional layers, fully connected networks, activation functions, and the OpenAI Gym Library, to the FHE domain.
Extensive experiments demonstrate that the proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance, as evidenced by the low mean absolute error (MAE) and high R-squared score compared to the plaintext counterpart.
By prioritizing privacy and security through FHE, the authors' approach paves the way for deploying UAVs in sensitive domains where data confidentiality is paramount.
Statistik
The mean absolute error (MAE) between the plaintext and FHE model intermediate outputs for each block in the network is less than 0.1.
The end-to-end FHE-based Reinforcement Learning framework achieves an R-squared score of 0.9631 compared to the unencrypted domain.
Kutipan
"Our proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance."
"By prioritizing privacy and security through FHE, our approach paves the way for deploying UAVs in sensitive domains where data confidentiality is paramount."