基于视觉联合学习的教育数据挖掘隐私保护技术

Privacy Protection Technology of Educational Data Mining Based on Visual Federated Learning

在过去十年中,教育越来越依赖人工智能。然而,在智能时代,隐私泄露已经成为一个必须立即解决的重要问题。为了实现这一目标,介绍了人工智能领域新兴的联合学习概念,分析了联合学习的定义、系统模型和训练过程以及隐私保护技术,并将联合学习与各种教育数据挖掘算法相结合。该框架可以在不集成数据的情况下对某些活动和情况执行加密训练,然后生成反映所有数据特征的可视化模型。联合可视化是联合学习体系结构在可视化领域的扩展。它主要侧重于在维护数据隐私的前提下,在对来自多个数据源的数据进行可视化分析时,部署一种互利双赢的联合合作技术。必须消除行业数据壁垒,共享数据和专业知识,以解决教育数据挖掘中可能出现的隐私保护问题。研究发现,联合学习方法原则上可以保护数据隐私,并且易于集成到现有的教育应用中。在保护隐私的基础上,使用联合可视化框架可以最大限度地提高模型的准确性。联合学习将为教育的信息化和智能化发展提供一条新的途径。

Artificial intelligence(AI)has become increasingly important in the field of education.However,with the rise of big data and in-telligence technology,ensuring privacy protection has become a crucial issue that needs immediate attention.To address this issue,the con-cept of federated learning in AI is introduced,which involves decentralized training of machine learning models on data that is distributed across multiple devices or organizations without exchanging the raw data.The system model and training process of federated learning,as well as its privacy protection technologies are analyzed,and the potential applications of the model in various educational data mining algo-rithms are explored.Federated visualization is an extension of the federated learning architecture in the domain of visualization,with a fo-cus on establishing mutually beneficial and win-win federated cooperation techniques for visual data analysis from multiple data sources while ensuring data privacy.The framework executes encryption training for certain activities and situations without integrating data,gener-ating a visual model that reflects the features of all data.By removing industry data barriers and sharing data and expertise,federated visu-alization can effectively address privacy protection issues that may arise in educational data mining.It is found that federated learning is a promising approach for protecting data privacy in principle and can be easily integrated into existing educational applications.Moreover,the use of a federated visualization framework can maximize model accuracy while protecting privacy.Federated learning provides a new pathway for the informatization and intelligent growth of education.