产生合成健康传感器数据,以保护隐私,可穿戴式压力检测
Lucas Lange1, Nils Wenzlitschke1, Erhard Rahm1
1ScaDS.AI Dresden/Leipzig, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究介绍了使用生成对抗网络 (GAN) 和差异隐私 (DP) 的隐私意识合成智能手表数据,以改进压力检测模型,同时保护敏感的健康信息.
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:
- 智能手表健康传感器数据对于智能健康应用和患者监测,包括压力检测至关重要.
- 获取用于研究的敏感智能手表健康数据是具有挑战性的,因为隐私问题和资源密集性.
- 现有的方法往往难以平衡数据实用性与强大的隐私保护.
研究的目的:
- 开发一种隐私意识的方法来合成与压力相关的多传感器智能手表健康数据.
- 提高研究数据的可用性,同时保护敏感的患者信息.
- 评估合成数据在提高压力检测模型性能方面的有效性.
主要方法:
- 使用生成对抗网络 (GANs) 来进行数据合成.
- 综合差异隐私 (DP) 保障措施,以确保患者数据的保护.
- 在压力检测任务中测试了来自多个GAN的合成数据,并采用了数据增强策略.
主要成果:
- 基于GAN的增强显著改善了应力检测模型的性能.
- 私人DP培训场景显示F1得分增加了11.90-15.48%.
- 非私人培训场景仍然观察到0.45%的绩效提升.
- 综合数据的完整性和可信性得到证实,尽管受到增加的隐私要求的影响.
结论:
- 不同的私有合成数据提供了一个有前途的方法来优化医疗信息学中公用事业-隐私权的权衡.
- 拟议的方法提高了研究数据的可用性,而不会显著损害患者的隐私.
- 合成数据生成对于增强有限的现实世界训练样本以检测压力是有效的.
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