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Tool-using LLM agents for health monitoring raise critical privacy concerns as they share sensitive patient data with cloud providers and third-party models. This study presents HealthAgent, a privacy-preserving LLM agent framework that protects both user queries and multi-modal sensor data through homomorphic encryption. HealthAgent enables an LLM orchestrator to coordinate specialized AI models for complex health assessments while processing all data in encrypted form. The system achieves 95% task decomposition accuracy with 10 s latency, demonstrating that strong privacy guarantees can be maintained without sacrificing real-time performance in health monitoring applications.

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Privacy-Preserving LLM Agent for Multi-modal Health Monitoring

  • 会议论文

  • 发表日期：

    2026年

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  • Qipeng Xie,
  • Jiafei Wu,
  • Weiyu Wang,
  • Zhuotao Lian,
  • Mu Yuan,
  • Xian Shuai,
  • Weizheng Wang,
  • Yuan Haoyi,
  • Haibo Hu,
  • Kaishun Wu

摘要

Tool-using LLM agents for health monitoring raise critical privacy concerns as they share sensitive patient data with cloud providers and third-party models. This study presents HealthAgent, a privacy-preserving LLM agent framework that protects both user queries and multi-modal sensor data through homomorphic encryption. HealthAgent enables an LLM orchestrator to coordinate specialized AI models for complex health assessments while processing all data in encrypted form. The system achieves 95% task decomposition accuracy with 10 s latency, demonstrating that strong privacy guarantees can be maintained without sacrificing real-time performance in health monitoring applications.