EAGER: Building a Provable Differentially Private Real-time Data-blind ML Algorithm: A case study on Enhancing STEM Student Engagement in Online Learning

EAGER：构建可证明的差分隐私实时数据盲机器学习算法：关于增强 STEM 学生在线学习参与度的案例研究

• 批准号: 2329919
• 负责人: Ashis Kumer Biswas
• 金额: $ 15万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329919&HistoricalAwards=false
• 关键词: EAGER; Building; Provable; Differentially; Private

The COVID-19 pandemic may be over, but transitions in course delivery format—going remote, or hybrid—are still being used and universities appreciate their potential to attract more diverse groups of students than purely on-campus classes. This flexible education format in platforms like Zoom is here to stay. To deliver better learning experiences, educators need to gauge students' engagement in courses. But, while lecturing, it is challenging to assess engagement online. Machine learning technology can help educators during lectures so that the classroom engagement dynamics can be estimated, and proper interventions can be taken in real time. However, data-driven machine learning (ML) technology puts its users at risk of privacy loss, even with distributed machine learning programs hosted in individual students’ personal workstations that learn patterns of their users and report the patterns back to a global learner that merges the resulting findings into a global ML model. Although no private data is leaving local workstations, the individual patterns distributed across the network can leak private data. This project will build innovative privacy-aware student-engagement detection technology. The main novelty of this project will be in its capacity to learn in real-time from various types of student engagement data without directly accessing it. In platformized online education, the project will add privacy guarantee to users, while underrepresented STEM students can safely interact with educators and peers to facilitate the community of inquiry model of learning.The project aims to design a distributed machine learning paradigm that introduces three hierarchical categories of learner nodes that will be facilitated by a novel neural network architecture agnostic gradient sharing algorithm that will make any coordinated attempt to reconstruct original data from the partial gradients shared between nodes provably intractable. The hierarchical organization of the framework makes it effective at providing a level of obfuscation in partial gradients coming from partially observable model architecture. The research methodology will be motivated by concepts of differential privacy in gradient sharing algorithms. The project will introduce new concepts regarding how to select the gradient components to distribute and to optimize learnable parameters without incurring any additional computational overhead in building a global model, compared to the state-of-the-art gradient-based defense algorithms. The project will be driven by two research thrusts: (1) design of a provable privacy-aware distributed machine learning framework, (2) leveraging the novel framework in estimating student engagement in platformized online STEM education at University of Colorado Denver. The research effort will solve an open problem in the distributed machine learning from a black-box perspective where both full gradients and model architecture are unknown. Therefore, it has potential to be adopted in other areas where privacy aware ML is a requirement. The project outcomes will provide immediate benefits to 1) undergraduate STEM students while improving student retention and overall learning experiences, 2) online STEM instructors who will be able to gauge student engagement in real-time with an equitable, privacy-aware and inclusive learning environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

共同19-19的大流行可能已经结束，但是在课程交付格式（即将到来的遥远或混合动力）中的过渡仍在使用，大学喜欢他们吸引更多多样化的学生的潜力，而不是纯粹是校园班级。在Zoom之类的平台中，这种灵活的教育格式将留在这里。为了提供更好的学习经验，教育工作者需要评估学生参与课程的参与。但是，在演讲时，在线评估参与是一项挑战。机器学习技术可以在讲座期间帮助教育工作者，以便可以估算课堂参与动态，并可以实时进行适当的干预措施。但是，数据驱动的机器学习（ML）技术使其用户处于隐私损失的危险中，即使分布式的机器学习计划托管在个别学生的个人工作站中，这些程序可以学习用户的模式，并将模式报告回到全球学习者，将结果结果融合到全球ML模型中。尽管没有私人数据离开本地工作站，但分布在整个网络上的单个模式会泄漏私人数据。该项目将建立创新的隐私感知学生参与检测技术。该项目的主要新颖性将其能力从各种类型的学生参与数据中实时学习，而无需直接访问它。 In platformized online education, the project will add privacy guarantee to users, while Underrepresented STEM students can safely interact with educators and peers to support the community of inquiry model of learning.The project aims to design a distributed machine learning paradigm that introduces three hierarchical categories of learner nodes that will be prepared by a novel neuronal network architecture agnostic gradient sharing algorithm that will make any coordinated attempt to reconstruct来自部分梯度的原始数据共享的节点之间可纠缠的疾病。该框架的层次结构组织使其有效地提供了来自部分可观察到的模型体系结构的部分梯度的混淆水平。该研究方法将由梯度共享算法中差异隐私的概念促进。与基于最先进的基于梯度的防御算法相比，该项目将介绍有关如何选择以分发和优化可学习参数的梯度组件并优化可学习参数的新概念。该项目将由两项研究推动力驱动：（1）设计可证明的隐私性分布式机器学习框架，（2）利用新颖的框架来估算科罗拉多大学丹佛大学平台化在线STEM教育的学生参与。研究工作将从分布式机器学习中从黑框的角度解决一个开放问题，在黑盒的角度上，完整的梯度和模型架构都是未知的。因此，它有可能在隐私意识ML的其他领域采用。该项目成果将为1）在改善学生的保留和整体学习经验的同时，2）在线STEM讲师提供直接的好处。