标题： 使用差分隐私提升学习分析中的隐私保护 显示英文标题

标题： Advancing privacy in learning analytics using differential privacy

摘要： 本文通过应用差分隐私（DP）提出了一种新的框架，解决了在学习分析（LA）中平衡学习者数据隐私与数据使用之间的挑战。 对更强大的隐私保护的需求不断增长，这是由不断变化的法律法规和日益增强的隐私担忧所驱动的，同时传统的匿名化方法对于教育数据的复杂性来说是不够的。 为了解决这个问题，我们引入了第一个专为LA设计的DP框架，并提供了其实现的实际指导。 我们通过一个LA使用场景展示了该框架的使用，并通过在一个知名LA数据集上的实验验证了DP在防止潜在攻击方面保护数据隐私的能力。 此外，我们探讨了在各种DP设置下数据隐私与效用之间的权衡。 我们的工作通过提供一个实用的DP框架，为LA领域做出了贡献，该框架可以支持研究人员和实践者在其工作中采用DP。 显示英文摘要

摘要： 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.