  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.
