Privacy-preserving machine learning for mental health prediction using homomorphic encryption

Student mental health issues, such as stress, anxiety, and depression, are increasingly prevalent in academic institutions, significantly affecting well-being and academic performance. Recent machine learning (ML)-based systems have demonstrated promise in predicting mental health conditions using survey data, but these approaches often process sensitive information in plaintext, risking privacy breaches or relying on centralized data storage vulnerable to leaks. Homomorphic encryption (HE) has been proposed for secure ML, but existing implementations either focus on simpler datasets (e.g., numerical/IoT data) or incur impractical computational overhead (e.g., high RAM usage or prolonged training times) for real-world mental health applications. To address these gaps, we introduce a privacy-preserving predictive model for student mental health using logistic regression trained directly on encrypted data via the TenSEAL library. Our work uniquely combines a leveled fully homomorphic encryption (FHE) scheme to ensure end-to-end confidentiality, replacing the standard sigmoid with a quadratic approximation for homomorphic compatibility. We also perform a comprehensive efficiency analysis that evaluates RAM usage and training time across polynomial-modulus degrees to balance security and practicality, a trade-off underexplored in prior HEbased mental health studies. Experimental results show that our encrypted model achieves 84% accuracy (vs. 96% unencrypted) with minimal performance loss, while benchmarks demonstrate scalable resource consumption. This work advances the feasibility of implementing FHE in sensitive domains such as mental health, offering a rigorous template for privacy-preserving ML without compromising predictive utility.