From Coordinates to Context: An LLM-Bootstrapped Semantic Encoding Framework for Privacy-Preserving Mobile Sensing Stress Recognition
For researchers in mobile sensing and privacy, this work provides a novel approach to balance privacy, utility, and explainability in location-based stress recognition, though it is domain-specific and incremental.
The paper introduces a privacy-preserving framework for stress recognition from GPS data that uses an LLM-bootstrapped semantic encoding to convert coordinates into human-readable features. The method achieves robust privacy protection (2-3x improvement) without statistically significant loss in stress recognition accuracy compared to non-private models.
Psychological stress is a widespread issue that significantly impacts student well-being and academic performance. Effective remote stress recognition is crucial, yet existing methods often rely on wearable devices or GPS-based clustering techniques that pose privacy risks and lack of human understandable explanations. In this study, we introduce a novel, end-to-end privacy-enhanced framework for semantic location encoding using a self-hosted OSM engine and an LLM-bootstrapped static map for human-friendly feature extraction, and pave a pathway for privacy-aware location data transformation for dataset sharing. We rigorously quantify the privacy-utility-explainability trilemma and demonstrate (via LOSO validation) that our Privacy-Aware (PA) model achieves robust privacy protection without being statistically distinguishable in stress recognition performance from a non-private model. Model explanation analysis highlights that our extracted features, which are user-friendly features, match with psychological literature about stress. In addition, an ablation study on the GeoLife dataset also demonstrates that our privacy framework improves privacy by 2-3 times compared to a non-privacy-aware approach. This suggests that our system can be utilized for the next generation of GPS transformations in open-source datasets for future researchers.