取消身份是不够的:取消身份和合成临床笔记之间的比较
Atiquer Rahman Sarkar1, Yao-Shun Chuang2, Noman Mohammed3
1Department of Computer Science, University of Manitoba, Winnipeg, R3T 5V6, Canada. sarkarar@myumanitoba.ca.
Scientific reports
|November 29, 2024
概括
不识别并不能完全保护临床笔记免受隐私攻击. 由大型语言模型生成的合成临床笔记在实现可比性能的情况下,显示与真实数据相似的隐私风险.
科学领域:
- 医疗信息学 医疗信息学
- 数据 隐私 数据 隐私 数据
- 人工智能的人工智能
背景情况:
- 匿名化是保护敏感临床数据隐私的一种常见方法.
- 合成数据生成正在成为保护隐私的替代方案.
- 生成模型的进步促使人们对合成临床笔记进行研究.
研究的目的:
- 评估非识别的临床笔记对成员推断攻击的隐私有效性.
- 开发一种使用大型语言模型生成合成临床笔记的新方法.
- 评估合成临床笔记在临床领域任务中的实用性及其隐私影响.
主要方法:
- 证明了非识别的临床笔记对会员推断攻击的脆弱性.
- 提出了一种新方法,用于合成临床笔记生成,利用最先进的大型语言模型.
- 评估了在临床任务上生成合成笔记的性能.
- 开发了一种方法来测试在合成数据上训练的模型的隐私风险.
主要成果:
- 不识别未能防止对真实临床笔记的会员推断攻击.
- 使用大型语言模型生成的合成临床笔记实现了与真实数据可比的性能.
- 合成票据的高性能与与真实数据相似的隐私问题相关.
- 即使在目标模型被训练在合成数据上,会员推断攻击也是可行的.
结论:
- 仅仅是取消身份是不足以强有力的隐私保护的临床笔记.
- 当前最先进的合成临床笔记虽然对研究任务有用,但继承了隐私风险.
- 需要进一步的研究来探索合成数据生成方法,以提供更好的隐私-实用性权衡.
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