LDP-GAN:具有局部差异隐私的生成对抗网络,用于患者医疗记录综合
Hansle Gwon1, Imjin Ahn1, Yunha Kim2
1Department of Information Medicine, Asan Medical Center, 8, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea.
Computers in biology and medicine
|November 23, 2023
概括
本研究引入了一种使用局部差异隐私 (LDP) 保护生成AI模型敏感训练数据的新方法. 它可以创建合成医疗数据,同时防止恶意攻击导致数据泄露.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 电子医疗记录 (EMR) 对推进医疗保健技术,特别是医疗人工智能有很大的潜力.
- 然而,与患者数据共享有关的隐私问题限制了EMR的充分利用.
- 深度学习生成模型可以创建合成数据,但面临来自攻击的训练数据泄露的风险.
研究的目的:
- 为解决与使用EMR数据用于训练AI模型相关的隐私风险.
- 提出一种新的方法来保护训练数据免受恶意攻击.
- 评估拟议方法的实用性和隐私性.
主要方法:
- 实施一种新的方法,将局部差异隐私 (LDP) 纳入其中,以保护AI培训模型.
- 保护生成模型免受恶意攻击以防止数据泄露.
- 对方法在数据实用性和隐私保护方面的性能进行评估.
主要成果:
- 拟议的方法有效地保护训练数据免受恶意攻击.
- 生成的合成医疗数据显示出合理的实用性.
- 该方法成功地平衡了数据生成能力和强大的隐私保护.
结论:
- 开发的基于LDP的方法为保护隐私的合成医疗数据生成提供了可行的解决方案.
- 这种方法增强了对人工智能开发EMR的安全利用.
- 它减轻了在深度学习模型的背景下敏感数据暴露的风险.
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