Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection
This work addresses privacy and data availability issues for researchers in smart health applications, though it is incremental as it combines existing methods like GANs and differential privacy.
The paper tackled the problem of generating synthetic health sensor data for wearable stress detection to address privacy concerns and data scarcity, achieving improvements in model performance with F1-score increases of 11.90-15.48% in private scenarios and 0.45% in non-private ones.
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.