{"ID":2835527,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.23200","arxiv_id":"2511.23200","title":"From Coordinates to Context: An LLM-Bootstrapped Semantic Encoding Framework for Privacy-Preserving Mobile Sensing Stress Recognition","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.","short_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....","url_abs":"https://arxiv.org/abs/2511.23200","url_pdf":"https://arxiv.org/pdf/2511.23200v2","authors":"[\"Hoang Khang Phan\",\"Nhat Tan Le\"]","published":"2025-11-28T14:04:00Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.HC\"]","methods":"[\"Large Language Model\"]","has_code":false}