# PrivLLMSwarm: Privacy-Preserving LLM-Driven UAV Swarms for Secure IoT Surveillance

arXiv: https://arxiv.org/abs/2512.06747 · HTML: https://mlsanity.com/paper/2512.06747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [PUMA: Secure Inference of LLaMA-7B in Five Minutes](https://mlsanity.com/paper/2307.12533/) (2023)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [TypeFly: Flying Drones with Large Language Model](https://mlsanity.com/paper/2312.14950/) (2023)
- [ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing](https://mlsanity.com/paper/2006.04593/) (2020)
- [Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design](https://mlsanity.com/paper/2312.01059/) (2023)
- [Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance](https://mlsanity.com/paper/2405.10391/) (2024)
- [A Survey on Security and Privacy Issues of UAVs](https://mlsanity.com/paper/2109.14442/) (2021)

## Abstract (arXiv)

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.