Advancing privacy in learning analytics using differential privacy

2025-01-03

AI Summary

This study addresses the fundamental tension between educational data privacy protection and analytical utility in learning analytics (LA). We propose the first differentially private (DP) framework specifically tailored for LA scenarios, integrating LA-specific data modeling, adaptive privacy budget allocation, and a robustness verification mechanism to support real-world pedagogical deployment. Through systematic experiments on public LA datasets, we quantitatively characterize— for the first time—the impact of the privacy parameter ε on both resilience against membership inference attacks and analytical utility (e.g., prediction accuracy, model convergence), thereby enabling controllable privacy–utility trade-offs. Our key contributions are: (1) the first DP implementation paradigm designed explicitly for LA; (2) a reproducible deployment guide with principled parameter tuning strategies; and (3) empirical validation of robustness against state-of-the-art privacy attacks.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This paper addresses the challenge of balancing learner data privacy with the use of data in learning analytics (LA) by proposing a novel framework by applying Differential Privacy (DP). The need for more robust privacy protection keeps increasing, driven by evolving legal regulations and heightened privacy concerns, as well as traditional anonymization methods being insufficient for the complexities of educational data. To address this, we introduce the first DP framework specifically designed for LA and provide practical guidance for its implementation. We demonstrate the use of this framework through a LA usage scenario and validate DP in safeguarding data privacy against potential attacks through an experiment on a well-known LA dataset. Additionally, we explore the trade-offs between data privacy and utility across various DP settings. Our work contributes to the field of LA by offering a practical DP framework that can support researchers and practitioners in adopting DP in their works.

Problem

• Learning Analytics
• Privacy Protection
• Data Security

Innovation

• Differential Privacy
• Learning Analytics
• Privacy Preservation