From Coordinates to Context: An LLM-Bootstrapped Semantic Encoding Framework for Privacy-Preserving Mobile Sensing Stress Recognition

TL;DR

This paper presents a privacy-preserving framework for stress recognition using semantic location encoding with LLM-bootstrapped maps, balancing privacy, utility, and explainability.

Contribution

It introduces a novel end-to-end privacy-aware location encoding method leveraging LLMs and open-source maps, enhancing privacy without sacrificing stress recognition accuracy.

Findings

Privacy-aware model matches non-private model in stress recognition performance.

The framework improves privacy by 2-3 times over non-private approaches.

Extracted features align with psychological stress literature.

Abstract

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.