Depressive Behavior Detection Using Sensor Signal Data: An Attention-based Privacy-Preserving Approach

Abstract

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.