From academia to industry: A framework to securely implement big data and AI to predict college graduates' employment trajectories

Abstract

The transition from academia to industry can be unpredictable, but what if we could forecast college graduate employment outcomes with both accuracy and robust security? This study introduces an innovative framework that leverages secure data analysis and machine learning to predict the employment trajectories of college graduates. By integrating homomorphic encryption, we safeguard the privacy of sensitive personal and academic data while enabling complex machine learning operations. Our approach involves meticulous data collection, feature engineering, encryption, and model development, resulting in a robust model that addresses privacy concerns without sacrificing prediction accuracy. We demonstrate our model's superiority over traditional approaches, achieving a notable increase in both security and stability. This research illuminates the potential of encrypted data analysis in reshaping predictive modeling methods, offering insights for educational institutions, policymakers, and students. Our findings not only address a pressing issue in employment forecasting but also lay the groundwork for secure and ethical big data applications across various domains.