Developing Innovative Privacy Learning Modules to Engage Students in Cybersecurity Education

开发创新的隐私学习模块，让学生参与网络安全教育

基本信息

批准号：
1712496
负责人：
Na Li
金额：
$ 30万
依托单位：
Prairie View A & M University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712496&HistoricalAwards=false
关键词：
Developing Innovative Privacy Learning Modules

项目摘要

The rapid development of Internet services such as cloud computing, social networks, and mobile applications has increased attention to privacy protection by both industry and academia. Demand for information security is at an all-time high, creating an urgent shortfall in the national workforce. Despite recognized and critical societal needs, privacy education has not been integrated satisfactorily into undergraduate Computer Science (CS) curricula. A serious lack of effective learning materials to help students understand critical privacy concepts and gain hands-on skills has created a barrier to expanding privacy education. To address this concern and better prepare qualified graduates for the future U.S. workforce, researchers from Prairie View A&M University (PVAMU) and the University of Tennessee at Chattanooga (UTC) will collaboratively develop hands-on learning materials on privacy protection and explore optimal integration of privacy into undergraduate cybersecurity education.This project serves the national interest by designing and develop innovative learning modules on cutting-edge privacy topics identified by federal funding priorities to respond to the lack of hands-on based curriculum in privacy and cybersecurity education. These new self-contained learning modules with real-world hands-on labs will not only enrich the CS curricula at the two participating institutions (PVAMU and UTC), but also ensure that they can be easily adopted by other institutions. With these modules, the PIs will revamp related CS core and elective courses, and develop a new course on privacy, which will have profound impacts on both the CS curriculum and student learning at PVAMU and UTC. As an HBCU, underrepresented minorities at PVAMU will benefit from this project. In addition, the project will evaluate the effectiveness of an experiential learning approach based on student learning outcomes, experiences, and motivation, and attitudes toward privacy issues. Project research data, developed learning modules, and teaching materials will be actively disseminated to the research and educational community to benefit other institutions and enrich the NSF cybersecurity learning material repository.

诸如云计算，社交网络和移动应用等互联网服务的快速开发使行业和学术界对隐私保护的关注增加了。信息安全的需求一直处于历史最高水平，在国家劳动力中造成了紧急的短缺。尽管公认和关键的社会需求，但隐私教育并未令人满意地纳入本科计算机科学（CS）课程。严重缺乏有效的学习材料来帮助学生了解关键的隐私概念并获得动手技能，这为扩大隐私教育的障碍带来了障碍。为了解决这一问题并更好地为未来的美国劳动力准备合格的毕业生，Prairie View A＆M大学（PVAMU）和田纳西大学查塔努加（UTC）的研究人员将开发有关隐私保护的动手学习材料，并探索私人范围的最佳融合，以探索私人的兴趣。联邦资助优先事项确定的主题可以响应缺乏基于动手的隐私和网络安全教育课程。这些新的具有现实世界实践实验室的新型独立学习模块不仅会在两个参与机构（PVAMU和UTC）上丰富CS课程，还可以确保其他机构可以轻松地采用它们。使用这些模块，PIS将修改相关的CS核心和选修课程，并开发有关隐私的新课程，这将对PVAMU和UTC的CS课程和学生学习产生深远的影响。作为HBCU，PVAMU的代表性不足将从该项目中受益。此外，该项目将根据学生的学习成果，经验和动力以及对隐私问题的态度评估经验学习方法的有效性。项目研究数据，开发的学习模块和教材将积极地传播到研究和教育社区，以使其他机构受益并丰富NSF网络安全学习材料存储库。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

IDEAL: An Interactive De-Anonymization Learning System

IDEAL：交互式去匿名学习系统

DOI：
10.1109/compsac48688.2020.0-210
发表时间：
2020
期刊：
and Applications Conference
影响因子：
0
作者：
Li, Na;Murugesan, Rajkumar;Li, Lin;Zheng, Hao
通讯作者：
Zheng, Hao

Efficiently discovering people connectivity in Online Social Networks

有效发现在线社交网络中的人员联系

DOI：
发表时间：
2020
期刊：
Online social networks and media
影响因子：
0
作者：
Li, Na;Das, Sajal K
通讯作者：
Das, Sajal K

Learning and Preserving Relationship Privacy in Photo Sharing

DOI：
10.1109/bdcat56447.2022.00029
发表时间：
2022-12
期刊：
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
影响因子：
0
作者：
Jialin Liu;Lin Li;Na Li
通讯作者：
Jialin Liu;Lin Li;Na Li

Facelock―A Labware for Teaching Photo Privacy in Online Social Networks Through Face Recognition

Facelock——一款通过人脸识别教授在线社交网络中照片隐私的实验室软件