# Advancing privacy in learning analytics using differential privacy

arXiv: https://arxiv.org/abs/2501.01786 · HTML: https://mlsanity.com/paper/2501.01786/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning](https://mlsanity.com/paper/2503.13550/) (2025)
- [A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning](https://mlsanity.com/paper/2603.13293/) (2026)
- [PEARL: A Task-Aware Framework for Evaluating Differentially Private Synthetic Educational Data](https://mlsanity.com/paper/2609.10612/) (2026)
- [Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores](https://mlsanity.com/paper/2609.29453/) (2026)

## Abstract (arXiv)

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.