Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable
  Stress Detection
Lucas Lange Nils Wenzlitschke Erhard Rahm
Smartwatch health sensor data is increasingly utilized in smart health
applications and patient monitoring, including stress detection. However, such
medical data often comprises sensitive personal information and is
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. Our method involves
the generation of synthetic sequence data through Generative Adversarial
Networks (GANs), coupled with the implementation of Differential Privacy (DP)
safeguards for protecting patient information during model training. To ensure
the integrity of our synthetic data, we employ a range of quality assessments
and monitor the plausibility between synthetic and original data. To test the
usefulness, we create private machine learning models on a commonly used,
albeit small, stress detection dataset, exploring strategies for enhancing the
existing data foundation with our synthetic data. Through our GAN-based
augmentation methods, we observe improvements in model performance, both in
non-private (0.45% F1) and private (11.90-15.48% F1) training scenarios. We
underline the potential of differentially private synthetic data in optimizing
utility-privacy trade-offs, especially with limited availability of real
training samples.