一个保护隐私的多用户检索系统,用于多模式的人工智能
Yifang Gao1, Wei Luo2, Chuanchuan Wang3
1School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Penang, Malaysia.
Scientific reports
|February 24, 2026
概括
本研究介绍了PMIRS,这是使用大型语言模型 (LLM) 进行保护隐私的多式联络检索的安全系统. 通过模糊,加密和联合学习,PMIRS提高了数据安全性,实现了高精度和低延迟.
科学领域:
- 人工智能的人工智能
- 计算机安全 计算机安全
- 信息检索 信息检索
背景情况:
- 大型语言模型 (LLM) 对人工智能至关重要,但在多用户云环境中可能会带来隐私风险.
- 安全和私密的多式联运数据检索是一个重大挑战.
研究的目的:
- 介绍PMIRS,这是一个新的系统,用于安全,以隐私为中心的多式联络图像和文本数据的检索.
- 为了使数据安全地传输到基于云的LLM,同时减轻隐私风险.
主要方法:
- PMIRS使用模糊化技术,加密推断 (AES-CBC) 和联合学习来微调基于CLIP的模型.
- 迪菲-赫尔曼算法确保了多用户设置中的安全密钥管理.
- 查询嵌入被通过区块智能投影模糊并被加密.
主要成果:
- 在一个定制的ImageNet数据集上,PMIRS实现了高达0.92的F1分数和超过0.90的精度.
- 检索延迟始终低于180毫秒.
- 与CLIP基线相比,PMIRS的平均F1得分提高了7.67%,保持了精度.
结论:
- PMIRS提供了一种实用的解决方案,用于安全,高效和保护隐私的多式联运检索.
- 该系统在医疗成像,客户服务和企业数据管理等领域具有潜在的应用,这些应用在GDPR和HIPAA等法规下进行.
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