在生成对抗网络下,个人健康数据保护和智能医疗应用
Xiaoyuan Gao1, Wei Mi2, Xirui Feng3
1College of Humanities and Law, Tianjin University of Science and Technology, Tianjin, 300222, China.
Scientific reports
|May 13, 2025
概括
本研究引入了基于差异隐私的医疗保健数据生成对抗网络 (DP-GAN-HD),以保护敏感的健康信息. DP-GAN-HD有效地平衡了数据隐私和实用性,在安全数据发布方面表现优于其他模型.
科学领域:
- 智能医疗保健是一个智能医疗保健.
- 数据 隐私 数据 隐私 数据
- 机器学习 机器学习
背景情况:
- 智能医疗保健的进步突出了对个人健康数据隐私保护的关键需求.
- 安全发布敏感的健康数据仍然是一个重大挑战.
研究的目的:
- 提出一种新的方法,DP-GAN-HD,将生成对抗网络 (GAN) 与差异隐私集成,以确保医疗数据的安全发布.
- 提高数据隐私与医疗数据实用性之间的平衡.
主要方法:
- 使用基于差异隐私的生成对抗网络用于医疗保健数据 (DP-GAN-HD).
- 采用多生成器架构,梯度剪切和基因算法来进行参数优化.
- 包含高斯噪声干扰来增强隐私.
主要成果:
- DP-GAN-HD实现了高精度 (例如,Kaggle心血管疾病数据集上的0.823) 的隐私预算为2.0,优于现有的差异性隐私模型.
- 随着隐私预算的增加,模型的准确性得到了提高,在3.0的预算中接近真实数据实用性.
- 显示了对隐私攻击的增强抵抗力,减少了泄漏风险.
结论:
- DP-GAN-HD有效地平衡了医疗保健数据集中的隐私保护和数据实用性.
- 拟议的模型可适应和有效用于需要安全数据处理的智能医疗保健应用程序.
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