如何无辜的预训练语言模型变成隐私陷
Ruixuan Liu1, Tianhao Wang2, Yang Cao3
1Emory University, Atlanta, USA.
Conference on Computer and Communications Security : proceedings of the ... conference on computer and communications security. ACM Conference on Computer and Communications Security
|May 22, 2025
概括
PreCurious框架揭示了恶意预训练模型如何损害微调数据隐私. 它强调了会员推断和数据提取的风险,即使有隐私保护措施.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 标准的预训练和微调范式被广泛用于语言模型.
- 社区平台可以轻松访问预先训练的模型,但缺乏严格的验证.
- 预先训练的模型可能会对微调数据集构成隐私风险.
研究的目的:
- 引入PreCurious框架,暴露一个新的攻击面.
- 展示攻击者如何利用预先训练的模型进行隐私攻击.
- 升级隐私风险,包括会员推断和数据提取.
主要方法:
- 提出PreCurious框架来攻击微调模型.
- 操纵训练前的记忆阶段.
- 使用欺骗性的配置引导微调.
主要成果:
- PreCurious绕过了参数效率和差异性私密微调的防御.
- 该框架使隐形隐私攻击成为可能.
- 即使在严格的差异性隐私预算下,数据提取也是可能的.
结论:
- 用户必须谨慎地从不值得信赖的来源下载预训练模型.
- 常识防御和教程可能不够.
- 即使是干净的数据集也可能容易受到提取攻击.
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