摘要
如何降低学习隐私的泄露风险,是在线学习面临的一个亟待解决的问题。文章首先揭露了基于学习资源学习热度的推荐系统的形成与隐私泄露风险问题;针对此问题,文章基于差分隐私保护理论,提出了差分隐私保护的学习资源学习热度推荐方案,即对学习资源推荐值进行差分隐私保护处理后再发布结果;最后,文章通过问卷统计分析和实验对比分析,验证了差分隐私保护的学习资源学习热度推荐效果。差分隐私保护的学习资源学习热度推荐的运用,既能保护学习者的学习隐私,又能保留较高的数据可用性,有利于促进在线学习的安全、健康发展。
How to reduce the leakage risk of learning privacy is an urgent problem waiting to be solved in online learning. Firstly, this paper disclosed the formation of the recommendation system based on the learning appeal of learning resources and the risk problem of privacy leakage. In view of this problem, a differential privacy-protecting learning appeal of learning resources recommendation programme was proposed, based on differential privacy protection theory, namely the learning resources recommendation values should be processed with differential privacy-protection before being published. Finally, the performance of the differential privacy-protecting learning appeal of learning resources recommendation was verified through statistical analysis of questionnaires and experimental comparative analysis. The application of the differential privacy-protecting learning appeal of learning resources recommendation could not only protect learners' learning privacy, but also preserve high data availability, which was conductive to promoting the safe and healthy development of online learning.
作者
刘梦君
贾玉娟
姜庆
LIU Meng-jun;JIA Yu-juan;JIANG Qing(School of Education,Hubei University,Wuhan,Hubei,China 430062;Smart Learning Research Center,Hubei University,Wuhan,Hubei,China 430062)
出处
《现代教育技术》
CSSCI
北大核心
2019年第5期99-105,共7页
Modern Educational Technology
基金
国家自然科学基金面上项目"面向移动位置服务的空间位置大数据差分隐私保护研究"(项目编号:41671443)
湖北省自然科学基金课题"移动环境下安全的位置服务关键技术研究"(项目编号:2017CFB136)资助
关键词
学习资源推荐
学习热度
学习隐私
差分隐私保护
learning resources recommendation
learning appeal
learning privacy
differential privacy-protecting
