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The integration of Artificial Intelligence (AI) and low-altitude Unmanned Aerial Vehicle (UAV) swarms enables powerful capabilities in real-time surveillance, environmental sensing, and collaborative decision-making. In this paper, we propose a Privacy-preserving Edge Inference (PPEI) framework tailored for UAV swarms, leveraging edge computing and Fully Homomorphic Encryption (FHE) to ensure end-to-end data confidentiality. A hierarchical secure data transmission architecture is designed to support encrypted information exchange among UAVs, edge nodes, and cloud servers. UAV sensory data are encrypted using a hybrid scheme that combines symmetric encryption for efficiency and asymmetric encryption for secure key distribution. All learning and inference tasks are conducted directly on FHE-encrypted data, producing ciphertext predictions that protect both mission-critical inputs and outputs from leakage. Furthermore, we integrate FHE-based model component encryption and differential privacy mechanisms to hide the model structure and weight parameters from untrusted inference environments and potential adversaries. Experimental results demonstrate that our FHE-based inference framework provides strong privacy guarantees with acceptable overhead.

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A Privacy-Preserving Edge Inference Framework for Low-Altitude UAV Swarm Intelligence

  • 会议论文

  • 发表日期：

    2026年

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  • Jianguo Chen,
  • Guoqing Xiao,
  • Longxin Zhang,
  • Guocheng Liao,
  • Bodong Wang,
  • Weijian You

摘要

The integration of Artificial Intelligence (AI) and low-altitude Unmanned Aerial Vehicle (UAV) swarms enables powerful capabilities in real-time surveillance, environmental sensing, and collaborative decision-making. In this paper, we propose a Privacy-preserving Edge Inference (PPEI) framework tailored for UAV swarms, leveraging edge computing and Fully Homomorphic Encryption (FHE) to ensure end-to-end data confidentiality. A hierarchical secure data transmission architecture is designed to support encrypted information exchange among UAVs, edge nodes, and cloud servers. UAV sensory data are encrypted using a hybrid scheme that combines symmetric encryption for efficiency and asymmetric encryption for secure key distribution. All learning and inference tasks are conducted directly on FHE-encrypted data, producing ciphertext predictions that protect both mission-critical inputs and outputs from leakage. Furthermore, we integrate FHE-based model component encryption and differential privacy mechanisms to hide the model structure and weight parameters from untrusted inference environments and potential adversaries. Experimental results demonstrate that our FHE-based inference framework provides strong privacy guarantees with acceptable overhead.