A Mental Health-Oriented Assessment Model and Dynamic Protection Mechanism for Users in Trajectory Data Lifecycle Privacy Management
Nan Fu, Lina Sheng, Yitong ZhouAbstract
Objective
This study aims to address privacy risks in trajectory data lifecycle management by developing a mental health-focused assessment model and dynamic protection mechanism, with particular attention to vulnerable groups including individuals with schizophrenia spectrum disorders. The research seeks to establish a comprehensive framework that integrates clinical psychological principles with privacy protection technologies.
Subjects and Methods
The study employed a mixed-methods approach involving 600 participants, including 50 individuals with diagnosed schizophrenia spectrum disorders. Phase 1 utilized standardized mental health assessments (SCL-90, PSQI) and customized privacy risk perception questionnaires to evaluate psychological vulnerability across data lifecycle stages. Phase 2 developed a machine learning model incorporating clinical psychological indicators, validated through controlled experiments simulating various privacy scenarios specific to mental health sensitivities.
Results
The assessment model achieved 89.2% accuracy in predicting privacy-induced psychological stress responses, with particularly strong performance for participants with schizophrenia spectrum traits (92.3% accuracy). Significant correlations were found between unauthorized data sharing and acute anxiety symptoms (p < 0.01). The dynamic protection mechanism reduced privacy-related paranoia and anxiety by 47% in vulnerable groups, with 40% improvement in overall mental well-being compared to standard approaches.
Conclusions
Integrating clinical mental health considerations into trajectory data protection effectively addresses psychological vulnerabilities while maintaining data utility. The proposed framework demonstrates particular value for protecting individuals with schizophrenia spectrum disorders, offering new insights for developing mental health-aware privacy preservation systems in clinical and community settings.
Acknowledgement
This research was supported by “Wuxi University Research Start-up Fund for High-level Talents (550225020)”.
Corresponding Author
Nan Fu, Wuxi University, Wuxi, Jiangsu, 214105, China.