Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Security concerns around using personally identifiable information (PII) introduces notable privacy concerns in sensor signal-based depression detection. In this study, we propose a novel attention-based privacy-preserving model that mitigates these concerns. It assigns greater weights to non-PII-releasing sensors and lesser to high-privacy risk sensors, leveraging the principles of differential privacy (DP). We compare the performance of machine learning and deep learning benchmark models with and without PII-releasing sensors. Our results underline a significant performance discrepancy, suggesting potential instability in prediction performance without these sensors. Our proposed model, with a recall, precision, F1 of 0.889, and an AUC of 0.9, illustrates that high-quality results are achievable while considering privacy. This privacy-conscious model holds substantial implications for promoting a more unobtrusive approach to mental healthcare. Furthermore, the model’s potential for secure deployment in wide-reaching digital health applications and collaborative settings enhances its relevance for large-scale mental monitoring while preserving privacy.
Recommended Citation
Yuan, Aijia; Garcia, Edlin; Zhu, Hongyi; and Samtani, Sagar, "Depressive Behavior Detection Using Sensor Signal Data: An Attention-based Privacy-Preserving Approach" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 5.
https://aisel.aisnet.org/hicss-58/cl/security/5
Depressive Behavior Detection Using Sensor Signal Data: An Attention-based Privacy-Preserving Approach
Hilton Waikoloa Village, Hawaii
Security concerns around using personally identifiable information (PII) introduces notable privacy concerns in sensor signal-based depression detection. In this study, we propose a novel attention-based privacy-preserving model that mitigates these concerns. It assigns greater weights to non-PII-releasing sensors and lesser to high-privacy risk sensors, leveraging the principles of differential privacy (DP). We compare the performance of machine learning and deep learning benchmark models with and without PII-releasing sensors. Our results underline a significant performance discrepancy, suggesting potential instability in prediction performance without these sensors. Our proposed model, with a recall, precision, F1 of 0.889, and an AUC of 0.9, illustrates that high-quality results are achievable while considering privacy. This privacy-conscious model holds substantial implications for promoting a more unobtrusive approach to mental healthcare. Furthermore, the model’s potential for secure deployment in wide-reaching digital health applications and collaborative settings enhances its relevance for large-scale mental monitoring while preserving privacy.
https://aisel.aisnet.org/hicss-58/cl/security/5