Ensuring Data Privacy in Teen and Children Mental Health Analytics: The Role of Differential Privacy in Data Engineering

Abstract

This article examines the critical intersection of youth mental health data analytics and privacy preservation technologies, focusing on differential privacy implementations. The article explores the growing mental health crisis among adolescent populations and the subsequent need for data-driven interventions while maintaining stringent privacy protections. It analyzes the effectiveness of differential privacy frameworks in protecting sensitive mental health information while enabling valuable research insights across educational and healthcare settings. The article comprehensively evaluates privacy-preserving mechanisms, including privacy budget management, noise addition techniques, and implementation challenges across various institutional contexts. An examination of real-world applications in school-based mental health programs and healthcare platforms it demonstrates how privacy-enhanced analytics can support effective mental health interventions while maintaining individual privacy and regulatory compliance.