{"ID":2831979,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06747","arxiv_id":"2512.06747","title":"PrivLLMSwarm: Privacy-Preserving LLM-Driven UAV Swarms for Secure IoT Surveillance","abstract":"Large Language Models (LLMs) are emerging as powerful enablers for autonomous reasoning and natural-language coordination in unmanned aerial vehicle (UAV) swarms operating within Internet of Things (IoT) environments. However, existing LLM-driven UAV systems process sensitive operational data in plaintext, exposing them to privacy and security risks. This work introduces PrivLLMSwarm, a privacy-preserving framework that performs secure LLM inference for UAV swarm coordination through Secure Multi-Party Computation (MPC). The framework incorporates MPC-optimized transformer components with efficient approximations of nonlinear activations, enabling practical encrypted inference on resource-constrained aerial platforms. A fine-tuned GPT-based command generator, enhanced through reinforcement learning in simulation, provides reliable instructions while maintaining confidentiality. Experimental evaluation in urban-scale simulations demonstrates that PrivLLMSwarm achieves high semantic accuracy, low encrypted inference latency, and robust formation control under privacy constraints. Comparative analysis shows PrivLLMSwarm offers a superior privacy-utility balance compared to differential privacy, federated learning, and plaintext baselines. To support reproducibility, the full implementation including source code, MPC components, and a synthetic dataset is publicly available. PrivLLMSwarm establishes a practical foundation for secure, LLM-enabled UAV swarms in privacy-sensitive IoT applications including smart-city monitoring and emergency response.","short_abstract":"Large Language Models (LLMs) are emerging as powerful enablers for autonomous reasoning and natural-language coordination in unmanned aerial vehicle (UAV) swarms operating within Internet of Things (IoT) environments. However, existing LLM-driven UAV systems process sensitive operational data in plaintext, exposing the...","url_abs":"https://arxiv.org/abs/2512.06747","url_pdf":"https://arxiv.org/pdf/2512.06747v1","authors":"[\"Jifar Wakuma Ayana\",\"Huang Qiming\"]","published":"2025-12-07T09:20:14Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Transformer\",\"Large Language Model\",\"Language Model\"]","has_code":false}