ScaDS.AI Dresden/Leipzig, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany.
Sensors (Basel). 2024 May 11;24(10):3052. doi: 10.3390/s24103052.
Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90-15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility-privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.
智能手表健康传感器数据在智能健康应用和患者监测中（包括压力检测）的应用越来越多。然而，此类医疗数据通常包含敏感的个人信息，并且出于研究目的获取这些数据的资源密集度很高。针对这一挑战，我们引入了一种基于生成对抗网络（GAN）和差分隐私（DP）保护的多传感器智能手表健康读数与压力时刻相关的隐私感知综合方法。我们的方法不仅保护了患者信息，还提高了研究数据的可用性。为了确保其有用性，我们测试了来自多个 GAN 的合成数据，并在实际的压力检测任务中采用了不同的数据增强策略。基于 GAN 的增强方法显著提高了模型性能，在私有 DP 训练场景中 F1 得分提高了 11.90-15.48%，而非私有训练场景的得分仍提高了 0.45%。这些结果表明，差分隐私合成数据在优化效用-隐私权衡方面具有潜力，特别是在真实训练样本有限的情况下。通过严格的质量评估，我们确认了我们的合成数据的完整性和合理性，然而，当增加隐私要求时，数据的质量会受到显著影响。
