Quantifying the Privacy-Utility Trade-off in GPS-based Daily Stress Recognition using Semantic Features
By: Hoang Khang Phan, Nhat Tan Le
Potential Business Impact:
Lets phones guess your stress without spying.
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. 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. We rigorously quantify the privacy-utility trade-off and demonstrate (via LOSO validation) that our Privacy-Aware (PA) model achieves performance statistically indistinguishable from a non-private model, proving that utility does not require sacrificing privacy. Feature importance analysis highlights that recreational activity time, working time, and travel time play a significant role in stress recognition.
Similar Papers
Identification of Emotionally Stressful Periods Through Tracking Changes in Statistical Features of mHealth Data
Methodology
Finds when older people feel stressed.
Decoding the Stressed Brain with Geometric Machine Learning
Machine Learning (CS)
Reads brain waves to find stress.
A Machine Learning-Based Multimodal Framework for Wearable Sensor-Based Archery Action Recognition and Stress Estimation
Machine Learning (CS)
Helps archers train better by tracking movement and stress.