适应性基于压缩的隐私保护大型语言模型,用于敏感的医疗保健
IEEE journal of biomedical and health informatics
|April 8, 2025
概括
本研究介绍了基于自适应压缩的保护隐私的LLM (ACP2LLM),这是一个新的医疗保健AI. ACP2LLM通过保护用户隐私并确保大型语言模型 (LLM) 的可靠响应来增强医疗咨询.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 为远程医疗咨询提供了先进的功能.
- 目前的LLM由于明确的数据上传而带来隐私风险,并且缺乏保证的响应可靠性.
研究的目的:
- 开发一种新的保护隐私的LLM,用于用户激活的健康咨询.
- 解决基于LLM的医疗保健中的隐私暴露和响应可靠性的挑战.
主要方法:
- 引入基于自适应压缩的保护隐私的LLM (ACP2LLM).
- 基于信息的自适应性令牌压缩方法的实施,以保护隐私.
- 利用一个多医生,一个首席医生机制,进行协作推断和隐私-实用性权衡.
主要成果:
- 在基于云的LLM咨询期间,ACP2LLM有效地保留了用户敏感信息.
- 该系统通过协作推理实现了有利的隐私-实用性权衡.
- 在各种代币压缩速率中表现出极具竞争力的性能.
结论:
- 在基于LLM的医疗保健中,ACP2LLM提供了强大的隐私保护能力.
- 拟议的方法实现了高答案精度,优于现有的LLM方法.
- ACP2LLM代表了安全可靠的人工智能驱动医疗咨询的重大进步.
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