Depressive Behavior Detection Using Sensor Signal Data: An Attention-based Privacy-Preserving Approach
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Date
2025-01-07
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406
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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.
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Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI, depression, machine learning, mental health, privacy, sensor signal
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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